A Comprehensive Overview of Large Language Models

Humza Naveeda , Asad Ullah Khanb,∗, Shi Qiuc,∗, Muhammad Saqibd,e,∗, Saeed Anwarf,g , Muhammad Usmanf,g , Naveed Akhtarh,j ,
Nick Barnesi , Ajmal Mianj
a The University of Sydney, Sydney, Australia
b University of Engineering and Technology (UET), Lahore, Pakistan
c The Chinese University of Hong Kong (CUHK), HKSAR, China
d University of Technology Sydney (UTS), Sydney, Australia
e Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, Australia
f King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia
g SDAIA-KFUPM Joint Research Center for Artificial Intelligence (JRCAI), Dhahran, Saudi Arabia
h The University of Melbourne (UoM), Melbourne, Australia
i Australian National University (ANU), Canberra, Australia
j The University of Western Australia (UWA), Perth, Australia
arXiv:2307.06435v10 [cs.CL] 17 Oct 2024

Abstract
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and
beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse
topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multi-modal LLMs,
robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in
LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering
the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise
yet comprehensive overview of the recent developments in this field. This article provides an overview of the literature on a broad
range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts
along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to provide not only a
systematic survey but also a quick, comprehensive reference for the researchers and practitioners to draw insights from extensive,
informative summaries of the existing works to advance the LLM research.
Keywords:
Large Language Models, LLMs, chatGPT, Augmented LLMs, Multimodal LLMs, LLM training, LLM Benchmarking

1. Introduction
Language plays a fundamental role in facilitating commu-
nication and self-expression for humans and their interaction
with machines. The need for generalized models stems from
the growing demand for machines to handle complex language
tasks, including translation, summarization, information re-
trieval, conversational interactions, etc. Recently, significant
breakthroughs have been witnessed in language models, pri-
marily attributed to transformers [1], increased computational
capabilities, and the availability of large-scale training data.
These developments have brought about a revolutionary trans-
formation by enabling the creation of LLMs that can approxi-
mate human-level performance on various tasks [2, 3]. Large

∗ Equalcontribution
Email addresses: humza_naveed@yahoo.com (Humza Naveed),
aukhanee@gmail.com (Asad Ullah Khan), shiqiu@cse.cuhk.edu.hk (Shi
Qiu), muhammad.saqib@data61.csiro.au (Muhammad Saqib),
saeed.anwar@kfupm.edu.sa (Saeed Anwar), Figure 1: The trend of papers released over the years containing keywords
muhammad.usman@kfupm.edu.sa (Muhammad Usman), "Large Language Model", "Large Language Model + Fine-Tuning", and "Large
naveed.akhtar1@unimelb.edu.au (Naveed Akhtar), Language Model + Alignment".
nick.barnes@anu.edu.au (Nick Barnes), ajmal.mian@uwa.edu.au
(Ajmal Mian)

Preprint submitted to Elsevier October 18, 2024

 Alpaca (Mar) LLaMA (Feb) DeepSeek (Jan)
CodeGen (Mar)
HuaTuo (Apr) Xuan Yuan 2.0 (May) Nemotron (Feb)
GPT-NeoX-20B (Apr)
Vicuna MPT (Jun) Grok-1 (Mar)
UL2 (May)
TK-Instruct (May) Koala (May) CodeT5+ Snowflake Arctic (Apr)
GLM (Oct)
mT0 (Dec) Wizard-LM StarCoder Mixtral 8x22B
PanGu-α (Apr) OPT LLaMA 2 (Jul) LLaMA 3
OPT-IML Wizard-Coder (Jun)
T5 (Oct) mT5 (Oct) T0 (Oct) CPM-2 (Jun) Galactica (Nov) Code Llama (Aug) DeepSeek-V2 (May)
Goat

2019 2020 2021 2022 2023 2024

Codex (Jul) MT-NLG (Jan) PanGu-Σ (Mar) Gemini-1.5 (Feb)

GPT-3 (May) WebGPT (Dec) ERNIE 3.0 Sparrow (Sep) AlphaCode (Feb) Bard (Oct) BloombergGPT GPT-4o (May) Grok-1.5 (Apr)
Jurassic-1 (Aug) FLAN-U-PaLM (Oct) Chinchilla (Mar) GPT-4 OpenAI o1 (Sep)
HyperCLOVA (Sep) ChatGPT (Nov) PaLM (Apr) Claude
Yuan 1.0 (Oct) AlexaTM (Aug) PaLM2 (May)
Gopher (Dec) U-PALM (Oct) Gemini (Dec)
ERNIE 3.0 Titan BLOOM (Nov)
GLaM
LaMDA

Figure 2: Chronological display of LLM releases: blue cards represent ‘pre-trained’ models, while orange cards correspond to ‘instruction-tuned’ models. Models
on the upper half signify open-source availability, whereas those on the bottom are closed-source. The chart illustrates the increasing trend towards instruction-tuned
and open-source models, highlighting the evolving landscape and trends in natural language processing research.

Language Models (LLMs) have emerged as cutting-edge arti- tool manipulation, question answering, autonomous agents, etc.
ficial intelligence systems that can process and generate text Various improvements have also been suggested in these areas
with coherent communication [4] and generalize to multiple either by task-specific training [25, 26, 27, 28, 29, 30, 31] or
tasks [5, 6]. better prompting [32].
The historical progress in natural language processing (NLP) The LLMs abilities to solve diverse tasks with human-level
evolved from statistical to neural language modeling and then performance come at the cost of slow training and inference,
from pre-trained language models (PLMs) to LLMs. While extensive hardware requirements, and higher running costs.
conventional language modeling (LM) trains task-specific mod- Such requirements have limited their adoption and opened up
els in supervised settings, PLMs are trained in a self-supervised opportunities to devise better architectures [15, 33, 34, 35]
setting on a large corpus of text [7, 8, 9] with the aim of learning and training strategies [36, 37, 21, 38, 39, 40, 41]. Param-
a generic representation that is shareable among various NLP eter efficient tuning [38, 41, 40], pruning [42, 43], quantiza-
tasks. After fine-tuning for downstream tasks, PLMs surpass tion [44, 45], knowledge distillation, and context length inter-
the performance gains of traditional language modeling (LM). polation [46, 47, 48, 49] among others are some of the methods
The larger PLMs bring more performance gains, which has led widely studied for efficient LLM utilization.
to the transitioning of PLMs to LLMs by significantly increas- Due to the success of LLMs on a wide variety of tasks, the
ing model parameters (tens to hundreds of billions) [10] and research literature has recently experienced a large influx of
training dataset (many GBs and TBs) [10, 11]. Following this LLM-related contributions. Researchers have organized the
development, numerous LLMs have been proposed in the lit- LLMs literature in surveys [50, 51, 52, 53], and topic-specific
erature [10, 11, 12, 6, 13, 14, 15]. An increasing trend in the surveys in [54, 55, 56, 57, 58]. In contrast to these surveys, our
number of released LLMs and names of a few significant LLMs contribution focuses on providing a comprehensive yet concise
proposed over the years are shown in Fig 1 and Fig 2, respec- overview of the general direction of LLM research. This arti-
tively. cle summarizes architectural and training details of pre-trained
The early work on LLMs, such as T5 [10] and mT5 [11] em- LLMs and delves deeper into the details of concepts like fine-
ployed transfer learning until GPT-3 [6] showed LLMs are tuning, multi-modal LLMs, augmented LLMs, datasets, eval-
zero-shot transferable to downstream tasks without fine-tuning. uation, applications, challenges, and others to provide a self-
LLMs accurately respond to task queries when prompted with contained comprehensive overview. Our key contributions are
task descriptions and examples. However, pre-trained LLMs summarized as follows.
fail to follow user intent and perform worse in zero-shot set-
tings than in few-shot. Fine-tuning them with task instruc- • We present a survey on the developments in LLM research,
tions data [16, 17, 18, 19] and aligning with human prefer- providing a concise, comprehensive overview of the direc-
ences [20, 21] enhances generalization to unseen tasks, im- tion.
proving zero-shot performance significantly and reducing mis- • We present extensive summaries of pre-trained models that
aligned behavior. include fine-grained details of architecture and training de-
In addition to better generalization and domain adaptation, tails.
LLMs appear to have emergent abilities, such as reasoning,
• We summarize major findings of the popular contributions
planning, decision-making, in-context learning, answering in
and provide a detailed discussion on the key design and
zero-shot settings, etc. These abilities are known to be ac-
development aspects of LLMs to help practitioners effec-
quired by them due to their gigantic scale even when the pre-
tively leverage this technology.
trained LLMs are not trained specifically to possess these at-
tributes [22, 23, 24]. Such abilities have led LLMs to be widely • In this self-contained article, we cover a range of con-
adopted in diverse settings, including multi-modal, robotics, cepts to present the general direction of LLMs compre-
hensively, including background, pre-training, fine-tuning,
2
Figure 3: A broader overview of LLMs, dividing LLMs into seven branches: 1. Pre-Training 2. Fine-Tuning 3. Efficient 4. Inference 5. Evaluation 6. Applications
7. Challenges

multi-modal LLMs, augmented LLMs, LLMs-powered utilization in different domains. Section 4 highlights the config-
agents, datasets, evaluation, etc. uration and parameters that play a crucial role in the function-
ing of these models. Summary and discussions are presented
We loosely follow the existing terminology to ensure a stan- in section 3.8. The LLM training and evaluation, datasets, and
dardized outlook of this research direction. For instance, fol- benchmarks are discussed in section 5, followed by challenges
lowing [50], our survey discusses pre-trained LLMs with 10B and future directions, and conclusion in sections 7 and 8, re-
parameters or more. We refer the readers interested in smaller spectively.
pre-trained models to [51, 52, 53].
The organization of this paper is as follows. Section 2 discusses
the background of LLMs. Section 3 focuses on LLMs overview,
architectures, training pipelines and strategies, fine-tuning, and
3
2. Background 2.4. Activation Functions
We provide the relevant background to understand the fun- The activation functions serve a crucial role in the curve-
damentals related to LLMs in this section. We briefly discuss fitting abilities of neural networks [69]. We discuss activation
necessary components in LLMs and refer the readers interested functions used in LLMs in this section.
in details to the original works. ReLU [70]: The Rectified linear unit (ReLU) is defined as:

2.1. Tokenization ReLU(x) = max(0, x) (1)

Tokenization [59] is an essential pre-processing step in
LLM training that parses the text into non-decomposing units GeLU [71]: The Gaussian Error Linear Unit (GeLU) is the
called tokens. Tokens can be characters, subwords [60], sym- combination of ReLU, dropout [72] and zoneout [73].
bols [61], or words, depending on the tokenization process. GLU variants [74]: The Gated Linear Unit [75] is a neural
Some of the commonly used tokenization schemes in LLMs network layer that is an element-wise product (⊗) of a linear
include wordpiece [62], byte pair encoding (BPE) [61], and un- transformation and a sigmoid transformed (σ) linear projection
igramLM [60]. Readers are encouraged to refer to [63] for a of the input given as:
detailed survey.
GLU(x, W, V, b, c) = (xW + b) ⊗ σ(xV + c), (2)
2.2. Encoding Positions
The transformer processes input sequences in parallel and where X is the input of layer and l, W, b, V and c are learned
independently of each other. Moreover, the attention mod- parameters. Other GLU variants [74] used in LLMs are:
ule in the transformer does not capture positional information.
As a result, positional encodings were introduced in trans- ReGLU(x, W, V, b, c) = max(0, xW + b)⊗,
former [64], where a positional embedding vector is added to GEGLU(x, W, V, b, c) = GELU(xW + b) ⊗ (xV + c),
the token embedding. Variants of positional embedding include S wiGLU(x, W, V, b, c, β) = S wishβ(xW + b) ⊗ (xV + c).
absolute, relative, or learned positional encodings. Within rel-
ative encoding, Alibi and RoPE are two widely used positional
2.5. Layer Normalization
embeddings in LLMs.
Alibi [65]: It subtracts a scalar bias from the attention score Layer normalization leads to faster convergence and is an in-
that increases with the distance between token positions. This tegrated component of transformers [64]. In addition to Layer-
favors using recent tokens for attention. Norm [76] and RMSNorm [77], LLMs use pre-layer normal-
RoPE [66]: It rotates query and key representations at an an- ization [78], applying it before multi-head attention (MHA).
gle proportional to the token absolute position in the input Pre-norm is shown to provide training stability in LLMs. An-
sequence, resulting in a relative positional encoding scheme other normalization variant, DeepNorm [79] fixes the issue with
which decays with the distance between the tokens. larger gradients in pre-norm.

2.3. Attention in LLMs

2.6. Distributed LLM Training
Attention assigns weights to input tokens based on impor-
tance so that the model gives more emphasis to relevant tokens. This section describes distributed LLM training approaches
Attention in transformers [64] calculates query, key, and value briefly. More details are available in [13, 37, 80, 81].
mappings for input sequences, where the attention score is Data Parallelism: Data parallelism replicates the model on
obtained by multiplying the query and key, and later used to multiple devices where data in a batch gets divided across de-
weight values. We discuss different attention strategies used in vices. At the end of each training iteration weights are synchro-
LLMs below. nized across all devices.
Self-Attention [64]: Calculates attention using queries, keys, Tensor Parallelism: Tensor parallelism shards a tensor compu-
and values from the same block (encoder or decoder). tation across devices. It is also known as horizontal parallelism
Cross Attention: It is used in encoder-decoder architectures, or intra-layer model parallelism.
where encoder outputs are the queries, and key-value pairs Pipeline Parallelism: Pipeline parallelism shards model layers
come from the decoder. across different devices. This is also known as vertical paral-
Sparse Attention [67]: Self-attention has O(n2 ) time complex- lelism.
ity which becomes infeasible for large sequences. To speed Model Parallelism: A combination of tensor and pipeline par-
up the computation, sparse attention [67] iteratively calculates allelism is known as model parallelism.
attention in sliding windows for speed gains. 3D Parallelism: A combination of data, tensor, and model par-
Flash Attention [68]: Memory access is the major bottleneck allelism is known as 3D parallelism.
in calculating attention using GPUs. To speed up, flash Optimizer Parallelism: Optimizer parallelism also known as
attention employs input tiling to minimize the memory reads zero redundancy optimizer [37] implements optimizer state
and writes between the GPU high bandwidth memory (HBM) partitioning, gradient partitioning, and parameter partitioning
and the on-chip SRAM. across devices to reduce memory consumption while keeping
the communication costs as low as possible.
4
2.7. Libraries
Some commonly used libraries for LLMs training are:
Transformers [82]: The library provides access to various pre-
trained transformer models with APIs to train, fine-tune, infer,
and develop custom models.
DeepSpeed [36]: A library for scalable distributed training and
inference of deep learning models.
Megatron-LM [80]: It provides GPU-optimized techniques for
large-scale training of LLMs. Figure 4: An example of attention patterns in language models, image is taken
JAX [83]: A Python library for high-performance numerical from [93].
computing and scaleable machine learning. It can differenti-
ate native Python and NumPy functions and execute them on
GPUs.
Colossal-AI [84]: A collection of components to write dis-
tributed deep learning models.
BMTrain [81]: A library to write efficient stand-alone LLMs
training code.
FastMoE [85]: Provides API to build mixture-of-experts
(MoE) model in PyTorch. Figure 5: An example of language model training objectives, image from [93].
MindSpore [86]: A deep learning training and inference frame-
work extendable to mobile, edge, and cloud computing.
the attention and the connection of transformer blocks. An il-
PyTorch [87]: A framework developed by Facebook AI Re-
lustration of attention patterns of these architectures is shown
search lab (FAIR) to build deep learning models. The main
in Figure 4.
features of PyTorch include a dynamic computation graph and
Encoder Decoder: This architecture processes inputs through
a pythonic coding style.
the encoder and passes the intermediate representation to the
Tensorflow [88]: A deep learning framework written by
decoder to generate the output. Here, the encoder sees the
Google. The key features of TensorFlow are graph-based com-
complete sequence utilizing self-attention whereas the decoder
putation, eager execution, scalability, etc.
processes the sequence one after the other with implementing
MXNet [89]: Apache MXNet is a deep learning framework
cross-attention.
with support to write programs in multiple languages, includ-
Causal Decoder: A type of architecture that does not have an
ing, Python, C++, Scala, R, etc. It also provides support for
encoder and processes and generates output using a decoder,
dynamic and static computation graphs.
where the predicted token depends only on the previous time
2.8. Data PreProcessing steps.
Prefix Decoder: It is also known as a non-causal decoder,
This section briefly summarizes data preprocessing tech-
where the attention calculation is not strictly dependent on the
niques used in LLMs training.
past information and the attention is bidirectional. An example
Quality Filtering: For better results, training data quality is
of a non-causal attention mask is shown in Figure 4.
essential. Some approaches to filtering data are: 1) classifier-
Mixture-of-Experts: It is a variant of transformer architecture
based and 2) heuristics-based. Classifier-based approaches
with parallel independent experts and a router to route tokens
train a classifier on high-quality data and predict the quality of
to experts. These experts are feed-forward layers after the at-
text for filtering, whereas heuristics-based employ some rules
tention block [90]. Mixture-of-Experts (MoE) is an efficient
for filtering like language, metrics, statistics, and keywords.
sparse architecture that offers comparable performance to dense
Data Deduplication: Duplicated data can affect model per-
models and allows increasing the model size without increas-
formance and increase data memorization; therefore, to train
ing the computational cost by activating only a few experts at a
LLMs, data deduplication is one of the preprocessing steps.
time [91, 92].
This can be performed at multiple levels, like sentences,
documents, and datasets.
Privacy Reduction: Most of the training data for LLMs is 2.10. Pre-Training Objectives
collected through web sources. This data contains private This section describes LLMs pre-training objectives. For
information; therefore, many LLMs employ heuristics-based more details see the paper [93].
methods to filter information such as names, addresses, and Full Language Modeling: An autoregressive language model-
phone numbers to avoid learning personal information. ing objective where the model is asked to predict future tokens
given the previous tokens, an example is shown in Figure 5.
Prefix Language Modeling: A non-causal training objective,
2.9. Architectures where a prefix is chosen randomly and only remaining target
Here we discuss the variants of the transformer architectures tokens are used to calculate the loss. An example is shown in
used in LLMs. The difference arises due to the application of Figure 5.
5
Figure 6: A basic flow diagram depicting various stages of LLMs from pre-training to prompting/utilization. Prompting LLMs to generate responses is possible at
different training stages like pre-training, instruction-tuning, or alignment tuning. “RL” stands for reinforcement learning, “RM” represents reward-modeling, and
“RLHF” represents reinforcement learning with human feedback.

Masked Language Modeling: In this training objective, tokens 2.12. LLMs Adaptation Stages
or spans (a sequence of tokens) are masked randomly and the
This section discusses the fundamentals of LLMs adaptation
model is asked to predict masked tokens given the past and
stages, from pre-training to fine-tuning for downstream tasks
future context. An example is shown in Figure 5.
and utilization. An example of different training stages and in-
Unified Language Modeling: Unified language modeling [94]
ference in LLMs is shown in Figure 6. In this paper, we refer
is a combination of causal, non-causal, and masked language
to alignment-tuning as aligning with human preferences, while
training objectives. Here in masked language modeling, the
occasionally the literature uses the term alignment for different
attention is not bidirectional but unidirectional, attending either
purposes.
left-to-right or right-to-left context.
2.12.1. Pre-Training
In the very first stage, the model is trained in a self-
2.11. LLMs Scaling Laws supervised manner on a large corpus to predict the next to-
kens given the input. The design choices of LLMs vary from
Scaling laws study the optimal combination of model param- encoder-decoder to decoder-only architectures with different
eters, dataset size, and computational resources that predict the building blocks and loss functions in sections 2.5, 2.4, 2.10.
improvement in the model performance. It has been shown
that the loss scales according to the power-law with model size, 2.12.2. Fine-Tuning
dataset size, and compute resources [95]. This study suggests There are different styles to fine-tune an LLM. This section
larger models are more important than big data for better perfor- briefly discusses fine-tuning approaches.
mance. Another variant of scaling law [96] suggests the model Transfer Learning: The pre-trained LLMs perform well for
size and the number of training tokens should be scaled equally. various tasks [6, 15]. However, to improve the performance for
6
a downstream task, pre-trained models are fine-tuned with the whereas to improve LLMs further on reasoning tasks many
task-specific data [10, 11], known as transfer learning. methods [16, 97] train them on reasoning datasets. We discuss
Instruction-tuning: To enable a model to respond to user various prompting techniques for reasoning below.
queries effectively, the pre-trained model is fine-tuned on in- Chain-of-Thought (CoT): A special case of prompting where
struction formatted data i.e., instruction and an input-output demonstrations contain reasoning information aggregated with
pair. Instructions generally comprise multi-task data in plain inputs and outputs so that the model generates outcomes with
natural language, guiding the model to respond according to the step-by-step reasoning. More details on CoT prompts are avail-
prompt and the input. This type of fine-tuning improves zero- able in [55, 103, 101].
shot generalization and downstream task performance. Details Self-Consistency: Improves CoT performance by generat-
on formatting instruction data and its various styles are avail- ing multiple responses and selecting the most frequent an-
able in [16, 50, 97]. swer [104].
Alignment-tuning: LLMs are prone to generating false, biased, Tree-of-Thought (ToT): Explores multiple reasoning paths
and harmful text. To make them helpful, honest, and harmless, with possibilities to look ahead and backtrack for problem-
models are aligned using human feedback. Alignment involves solving [105].
asking LLMs to generate unexpected responses and then updat- Single-Turn Instructions: In this prompting setup, LLMs are
ing their parameters to avoid such responses [20, 21, 98]. queried only once with all the relevant information in the
It ensures LLMs operate according to human intentions and prompt. LLMs generate responses by understanding the con-
values. A model is defined to be an “aligned” model if the text either in a zero-shot or few-shot setting.
model fulfills three criteria of helpful, honest, and harmless or Multi-Turn Instructions: Solving a complex task requires mul-
“HHH” [99]. tiple interactions with LLMs, where feedback and responses
Researchers employ reinforcement learning with human feed- from the other tools are given as input to the LLM for the next
back (RLHF) [100] for model alignment. In RLHF, a fine-tuned rounds. This style of using LLMs in the loop is common in
model on demonstrations is further trained with reward model- autonomous agents.
ing (RM) and reinforcement learning (RL), shown in Figure 6.
Below we briefly discuss RM and RL pipelines in RLHF.
3. Large Language Models
Reward modeling: trains a model to rank generated responses
according to human preferences using a classification objec- This section reviews LLMs, briefly describing their architec-
tive. To train the classifier humans annotate LLMs generated tures, training objectives, pipelines, datasets, and fine-tuning
responses based on the HHH criteria. details.
Reinforcement learning: in combination with the reward model
is used for alignment in the next stage. The previously trained
3.1. Pre-Trained LLMs
reward model ranks LLM-generated responses into preferred
vs. non-preferred, which is used to align the model with proxi- Here, we provide summaries of various well-known pre-
mal policy optimization (PPO). This process repeats iteratively trained LLMs with significant discoveries, changing the course
until convergence. of research and development in NLP. These LLMs have consid-
erably improved the performance in NLU and NLG domains,
2.12.3. Prompting/Utilization and are widely fine-tuned for downstream tasks. Moreover, We
Prompting is a method to query trained LLMs for generating also identify key findings and insights of pre-trained LLMs in
responses, as illustrated in Figure 6. LLMs can be prompted in Table 1 and 2 that improve their performance.
various prompt setups, where they can be adapted to the instruc-
tions without fine-tuning and in other cases with fine-tuning on 3.1.1. General Purpose
data containing different prompt styles [16, 101, 102]. A good T5 [10]: An encoder-decoder model employing a unified text-
guide on prompt engineering is available at [32]. Below, we to-text training for all NLP problems is shown in Figure 7. T5
will discuss various widely used prompt setups. places layer normalization outside the residual path in a conven-
Zero-Shot Prompting: LLMs are zero-shot learners and ca- tional transformer model [64]. It uses masked language mod-
pable of answering queries never seen before. This style of eling as a pre-training objective where spans (consecutive to-
prompting requires LLMs to answer user questions without see- kens) are replaced with a single mask instead of separate masks
ing any examples in the prompt. for each token. This type of masking speeds up the training as
In-context Learning: Also known as few-shot learning, here, it produces shorter sequences. After pre-training, the model is
multiple input-output demonstration pairs are shown to the fine-tuned using adapter layers [106] for downstream tasks.
model to generate the desired response. This adaptation style GPT-3 [6]: The GPT-3 architecture is the same as the GPT-
is also called few-shot learning. A discussion on formatting in- 2 [5] but with dense and sparse attention in transformer layers
context learning (ICL) templates is available in [54, 50, 18, 16]. similar to the Sparse Transformer [67]. It shows that large mod-
Reasoning in LLMs: LLMs are zero-shot reasoners and can els can train on larger batch sizes with a lower learning rate to
be provoked to generate answers to logical problems, task decide the batch size during training, GPT-3 uses the gradient
planning, critical thinking, etc. with reasoning. Generating noise scale as in [107]. Overall, GPT-3 increases model param-
reasons is possible only by using different prompting styles, eters to 175B showing that the performance of large language
7
 plete fine-tuning and prompt fine-tuning as in [40] where only
prompt-related parameters are updated by inserting prompts at
various positions, front, middle, and back. CPM-2 also pro-
poses the INFMOE, a memory-efficient framework with a strat-
egy to dynamically offload parameters to the CPU for inference
at a 100B scale. It overlaps data movement with inference com-
putation for lower inference time.
ERNIE 3.0 [110]: ERNIE 3.0 takes inspiration from multi-
Figure 7: Unified text-to-text training example, source image from [10]. task learning to build a modular architecture using Transformer-
XL [111] as the backbone. The universal representation mod-
ule is shared by all the tasks, which serve as the basic block
for task-specific representation modules, which are all trained
jointly for natural language understanding, natural language
generation, and knowledge extraction. This LLM is primar-
ily focused on the Chinese language. It claims to train on the
largest Chinese text corpora for LLM training, and achieved
state-of-the-art in 54 Chinese NLP tasks.
Jurassic-1 [112]: A pair of auto-regressive language mod-
els, including a 7B-parameter J1-Large model and a 178B-
parameter J1-Jumbo model. The training vocabulary of
Jurassic-1 comprise word pieces, complete words, and multi-
word expressions without any word boundaries, where possible
out-of-vocabulary instances are interpreted as Unicode bytes.
Compared to the GPT-3 counterparts, the Jurassic-1 models
Figure 8: The image is the article of [108], showing an example of PanGu-α apply a more balanced depth-to-width self-attention architec-
architecture.
ture [113] and an improved tokenizer for a faster prediction
based on broader resources, achieving a comparable perfor-
models improves with the scale and is competitive with the fine- mance in zero-shot learning tasks and a superior performance in
tuned models. few-shot learning tasks given the ability to feed more examples
mT5 [11]: A multilingual T5 model [10] trained on the mC4 as a prompt.
dataset with 101 languages. The dataset is extracted from the HyperCLOVA [114]: A Korean language model with GPT-3
public common crawl scrape. The model uses a larger vocab- architecture.
ulary size of 250,000 to cover multiple languages. To avoid Yuan 1.0 [115]: Trained on a Chinese corpus with 5TB of
over-fitting or under-fitting for a language, mT5 employs a data high-quality text collected from the Internet. A Massive Data
sampling procedure to select samples from all languages. The Filtering System (MDFS) built on Spark is developed to pro-
paper suggests using a small amount of pre-training datasets, cess the raw data via coarse and fine filtering techniques. To
including all languages when fine-tuning for a task using En- speed up the training of Yuan 1.0 to save energy expenses and
glish language data. This allows the model to generate correct carbon emissions, various factors that improve the performance
non-English outputs. of distributed training are incorporated in architecture and train-
PanGu-α [108]: An autoregressive model that has a query ing: like increasing the hidden state size improves pipeline and
layer at the end of standard transformer layers, example shown tensor parallelism performance, larger micro batches improve
in Figure 8, to predict the next token. Its structure is similar to pipeline parallelism performance, and larger global batch size
the transformer layer but with an additional embedding for the improve data parallelism performance. In practice, the Yuan 1.0
next position in the attention mechanism, given in Eq. 3. model performs well on text classification, Winograd Schema,
natural language inference, and reading comprehension tasks.
a = pn Whq Whk T HLT (3) Gopher [116]: The Gopher family of models ranges from
44M to 280B parameters in size to study the effect of scale
CPM-2 [12]: Cost-efficient Pre-trained language Models on the LLMs performance. The 280B model beats GPT-3 [6],
(CPM-2) pre-trains bilingual (English and Chinese) 11B and Jurrasic-1 [112], MT-NLG [117], and others on 81% of the
198B mixture-of-experts (MoE) models on the WuDaoCor- evaluated tasks.
pus [109] dataset. The tokenization process removes “_” white ERNIE 3.0 TITAN [35]: ERNIE 3.0 Titan extends ERNIE 3.0
space tokens in the sentencepiece tokenizer. The models are by training a larger model with 26x the number of parameters
trained with knowledge inheritance, starting with only the Chi- of the latter. This bigger model outperformed other state-of-the-
nese language in the first stage and then adding English and art models in 68 NLP tasks. LLMs produce text with incorrect
Chinese data. This trained model gets duplicated multiple times facts. In order to have control of the generated text with fac-
to initialize the 198B MoE model. Moreover, to use the model tual consistency, ERNIE 3.0 Titan adds another task, Credible
for downstream tasks, CPM-2 experimented with both com- and Controllable Generations, to its multi-task learning setup.
8
It introduces additional self-supervised adversarial and control-
lable language modeling losses to the pre-training step, which
enables ERNIE 3.0 Titan to beat other LLMs in their manually
selected Factual QA task set evaluations.
GPT-NeoX-20B [118]: An auto-regressive model that largely
follows GPT-3 with a few deviations in architecture design,
trained on the Pile dataset without any data deduplication. GPT-
NeoX has parallel attention and feed-forward layers in a trans-
former block, given in Eq. 4, that increases throughput by 15%.
It uses rotary positional embedding [66], applying it to only
25% of embedding vector dimension as in [119]. This reduces
the computation without performance degradation. As opposed Figure 9: The BLOOM architecture example sourced from [13].
to GPT-3, which uses dense and sparse layers, GPT-NeoX-20B
uses only dense layers. The hyperparameter tuning at this scale
is difficult; therefore, the model chooses hyperparameters from relationship that model size should be doubled for every dou-
the method [6] and interpolates values between 13B and 175B bling of training tokens. Over 400 language models ranging
models for the 20B model. The model training is distributed from 70 million to over 16 billion parameters on 5 to 500 bil-
among GPUs using both tensor and pipeline parallelism. lion tokens are trained to get the estimates for compute-optimal
training under a given budget. The authors train a 70B model
x + Attn(LN1 (x)) + FF(LN2 (x)) (4) with the same compute budget as Gopher (280B) but with 4
times more data. It outperforms Gopher [116], GPT-3 [6], and
OPT [14]: It is a clone of GPT-3, developed to open-source others on various downstream tasks, after fine-tuning.
a model that replicates GPT-3 performance. Training of OPT AlexaTM [122]: An encoder-decoder model, where encoder
employs dynamic loss scaling [120] and restarts from an earlier weights and decoder embeddings are initialized with a pre-
checkpoint with a lower learning rate whenever loss divergence trained encoder to speed up training. The encoder stays frozen
is observed. Overall, the performance of OPT-175B models is for the initial 100k steps and is later unfrozen for end-to-end
comparable to the GPT3-175B model. training. The model is trained on a combination of denoising
BLOOM [13]: A causal decoder model trained on the ROOTS and causal language modeling (CLM) objectives, concatenat-
corpus to open-source an LLM. The architecture of BLOOM is ing a [CLM] token at the beginning for mode switching. Dur-
shown in Figure 9, with differences like ALiBi positional em- ing training, the CLM task is applied for 20% of the time, which
bedding, an additional normalization layer after the embedding improves the in-context learning performance.
layer as suggested by the bitsandbytes1 library. These changes PaLM [15]: A causal decoder with parallel attention and
stabilize training with improved downstream performance. feed-forward layers similar to Eq. 4, speeding up training by
GLaM [91]: Generalist Language Model (GLaM) represents a a factor of 15. Additional changes to the conventional trans-
family of language models using a sparsely activated decoder- former model include SwiGLU activation, RoPE embeddings,
only mixture-of-experts (MoE) structure [121, 90]. To gain multi-query attention that saves computation cost during decod-
more model capacity while reducing computation, the experts ing, and shared input-output embeddings. During training, loss
are sparsely activated where only the best two experts are used spiking was observed, and to fix it, model training was restarted
to process each input token. The largest GLaM model, GLaM from a 100-step earlier checkpoint by skipping 200-500 batches
(64B/64E), is about 7× larger than GPT-3 [6], while only part of around the spike. Moreover, the model was found to memo-
the parameters are activated per input token. The largest GLaM rize around 2.4% of the training data at the 540B model scale,
(64B/64E) model achieves better overall results as compared whereas this number was lower for smaller models.
to GPT-3 while consuming only one-third of GPT-3’s training PaLM-2 [123]: A smaller multi-lingual variant of PaLM,
energy. trained for larger iterations on a better quality dataset. PaLM-
MT-NLG [117]: A 530B causal decoder based on the GPT- 2 shows significant improvements over PaLM, while reducing
2 architecture that has roughly 3× GPT-3 model parameters. training and inference costs due to its smaller size. To lessen
MT-NLG is trained on filtered high-quality data collected from toxicity and memorization, it appends special tokens with a
various public datasets and blends various types of datasets in a fraction of pre-training data, which shows a reduction in gener-
single batch, which beats GPT-3 on several evaluations. ating harmful responses.
Chinchilla [96]: A causal decoder trained on the same dataset U-PaLM [124]: This method trains PaLM for 0.1% addi-
as the Gopher [116] but with a little different data sampling tional compute with the UL2 (also named as UL2Restore) ob-
distribution (sampled from MassiveText). The model architec- jective [125], using the same dataset it outperforms the baseline
ture is similar to the one used for Gopher, with the exception of significantly on various NLP tasks, including zero-shot, few-
AdamW optimizer instead of Adam. Chinchilla identifies the shot, commonsense reasoning, CoT, etc. Training with UL2R
involves converting a causal decoder PaLM to a non-causal de-
coder PaLM and employing 50% sequential denoising, 25%
1 https://github.com/TimDettmers/bitsandbytes regular denoising, and 25% extreme denoising loss functions.
9
 UL2 [125]: An encoder-decoder architecture trained using a the computation significantly.
mixture of denoisers (MoD) objective. Denoisers include 1) Grok [133, 134]: Grok is a family of LLMs including Grok-1
R-Denoiser: a regular span masking, 2) S-Denoiser: which cor- and Grok-1.5, released by XAI.
rupts consecutive tokens of a large sequence and 3) X-Denoiser: Grok-1 [133]: Grok-1 is a 314B parameters language MoE
which corrupts a large number of tokens randomly. During pre- model (eight experts), where two experts are activated per to-
training, UL2 includes a denoiser token from R, S , X to rep- ken.
resent a denoising setup. It helps improve fine-tuning perfor- Grok-1.5 [134]: Grok-1.5 is a multi-modal LLM with a larger
mance for downstream tasks that bind the task to one of the up- context length and improved performance.
stream training modes. This MoD style of training outperforms Gemini [135, 136]: Gemini replaces Bard (based on PaLM)
the T5 model on many benchmarks. with multi-modal capabilities and significant language model-
GLM-130B [33]: GLM-130B is a bilingual (English and Chi- ing performance improvements.
nese) model trained using an auto-regressive mask infilling pre-
Gemini-1 [135]: The first-ever auto-regressive model to
training objective similar to the GLM [126]. This training style
achieve human-level capabilities on the MMLU benchmark.
makes the model bidirectional as compared to GPT-3, which is
Gemini-1.5 [136]: A multi-modal LLM with MoE architec-
unidirectional. As opposed to GLM, the training of GLM-130B
ture builds on the findings of Gemini-1. The model has a 2M
includes a small amount of multi-task instruction pre-training
context window and can reason over information up to 10M
data (5% of the total data) along with self-supervised mask in-
tokens. Such large context windows were never achieved pre-
filling. To stabilize the training, it applies embedding layer gra-
viously and shown to have a huge impact on performance gain.
dient shrink.
LLaMA [127, 21]: A set of decoder-only language models Nemotron-4 340B [137]: A decoder-only model that has been
varying from 7B to 70B parameters. LLaMA models series is aligned on 98% synthetic data and only 2% manually annotated
the most famous among the community for parameter efficiency data. Utilizing synthetic data at a large proportion improves the
and instruction tuning. model performance significantly. The paper suggested intro-
LLaMA-1 [127]: Implements efficient causal attention [128] ducing alignment data with a smaller subset of previously seen
by not storing and computing masked attention weights and data during the late stage of the model pre-training, enabling the
key/query scores. Another optimization is reducing the number smooth transition from the pre-trained stage to the final train-
of activations recomputed in the backward pass, as in [129]. ing stage. To train better instruction-following models, weaker
LLaMA-2 [21]: This work is more focused on fine-tuning a models are trained into stronger models iteratively. The syn-
safer and better LLaMA-2-Chat model for dialogue generation. thetic data generated by the weaker instruction-tuned model is
The pre-trained model has 40% more training data with a larger used to train a base model which is later supervised fine-tuned
context length and grouped-query attention. outperforming the weaker model.
LLaMA-3/3.1 [130]: A collection of models trained on a DeepSeek [138]: DeepSeek studies the LLMs scaling laws
seven times larger dataset as compared to LLaMA-2 with dou- in detail to determine the optimal non-embedding model size
ble the context length, outperforming its previous variants and and training data. The experiments were performed for 8 bud-
other models. gets ranging from 1e17 to 3e20 training FLOPs. Each compute
PanGu-Σ [92]: An autoregressive model with parameters budget was tested against ten different models/data scales. The
copied from PanGu-α and extended to a trillion scale with Ran- batch size and learning rates were also fitted for the given com-
dom Routed Experts (RRE), the architectural diagram is shown pute budget finding that the batch size should increase with
in Figure 10. RRE is similar to the MoE architecture, with the increased compute budget while decreasing the learning
distinctions at the second level, where tokens are randomly rate. Following are the equations for the optimal batch-size (B),
routed to experts in a domain instead of using a learnable gat- learning rate (η), model size (M), and data (D):
ing method. The model has bottom layers densely activated and
shared across all domains, whereas top layers are sparsely ac- Bopt = 0.2920.C 0.3271
tivated according to the domain. This training style allows for ηopt = 0.3118.C −0.1250
extracting task-specific models and reduces catastrophic forget- Mopt = Mbase .C a (5)
ting effects in the case of continual learning.
Mixtral8x22b [131]: A mixture-of-experts (MoE) model with Dopt = Dbase .C b

eight distinct experts routes each token to two experts at each Mbase = 0.1715, Dbase = 5.8316, a = 0.5243, b = 0.4757
layer and combines the outputs additively.
Snowflake Arctic [132]: Arctic LLM is a hybrid of dense and DeepSeek-v2 [139]: An MoE model that introduces multi-
mixture-of-experts (MoE) architecture. The MoE (128×3.66B head latent attention (MLA) to reduce inference costs, by com-
MLP experts) is parallel to the dense transformer (10B) with pressing Key-Value (KV) cache into a latent vector. MLA
only two experts activated. The model has many experts, com- achieves better performance than multi-head attention (MHA),
pared to other MoE LLMs [131, 133], to increase the model and other efficient attention mechanisms such as grouped query
capacity and provide an opportunity to choose among many ex- attention (GQA), multi-query attention (MQA), etc. Because
perts for a diverse configuration. The model has 480B param- of MLA, DeepSeek-v2 achieves 5.76 times faster inference
eters, and only 17B are active during a forward pass, reducing throughput as compared to DeepSeek [138].
10
3.1.2. Coding emails, and other personal data from the training data. Its fine-
CodeGen [140]: CodeGen has a similar architecture to tuned variant outperforms PaLM, LLaMA, and LAMDA on
PaLM [15], i.e., parallel attention, MLP layers, and RoPE em- HumanEval and MBPP benchmarks.
beddings. The model is trained on both natural language and
programming language data sequentially (trained on the first 3.1.3. Scientific Knowledge
dataset, then the second, and so on) on the following datasets Galactica [148]: A large curated corpus of human scientific
1) PILE, 2) BIGQUERY, and 3) BIGPYTHON. CodeGen pro- knowledge with 48 million papers, textbooks, lecture notes,
posed a multi-step approach to synthesizing code. The purpose millions of compounds and proteins, scientific websites, en-
is to simplify the generation of long sequences where the previ- cyclopedias, and more are trained using the metaseq library3,
ous prompt and generated code are given as input with the next which is built on PyTorch and fairscale [149]. The model wraps
prompt to generate the next code sequence. CodeGen open- reasoning datasets with the < work > token to provide step-by-
source a Multi-Turn Programming Benchmark (MTPB) to eval- step reasoning context to the model, which has been shown to
uate multi-step program synthesis. improve the performance on reasoning tasks.
Codex [141]: This LLM is trained on a subset of public Python
Github repositories to generate code from docstrings. Com-
3.1.4. Dialog
puter programming is an iterative process where the programs
are often debugged and updated before fulfilling the require- LaMDA [150]: A decoder-only model pre-trained on pub-
ments. Similarly, Codex generates 100 versions of a program lic dialog data, public dialog utterances, and public web doc-
by repetitive sampling for a given description, which produces uments, where more than 90% of the pre-training data is in
a working solution for 77.5% of the problems passing unit tests. English. LaMDA is trained with the objective of producing re-
Its powerful version powers Github Copilot2 . sponses that exhibit high levels of quality, safety, and grounded-
ness. To achieve this, discriminative and generative fine-tuning
AlphaCode [142]: A set of large language models, ranging
techniques are incorporated to enhance the model’s safety and
from 300M to 41B parameters, designed for competition-level
quality aspects. As a result, the LaMDA models can be utilized
code generation tasks. It uses the multi-query attention [143] to
as a general language model performing various tasks.
reduce memory and cache costs. Since competitive program-
ming problems highly require deep reasoning and an under-
standing of complex natural language algorithms, the Alpha- 3.1.5. Finance
Code models are pre-trained on filtered GitHub code in popular BloombergGPT [151]: A non-causal decoder model trained
languages and then fine-tuned on a new competitive program- using both financial (“FINPILE” from the Bloomberg archive)
ming dataset named CodeContests. The CodeContests dataset and general-purpose datasets. The model’s architecture is sim-
mainly contains problems, solutions, and test cases collected ilar to the BLOOM [13] and OPT [14]. It allocates 50B param-
from the Codeforces platform3 . The pre-training employs stan- eters to different blocks of the model using the approach [113].
dard language modeling objectives, while GOLD [144] with For effective training, BloombergGPT packs documents to-
tempering [145] serves as the training objective for the fine- gether with < |endo f text| > to use the maximum sequence
tuning on CodeContests data. To evaluate the performance of length, uses warmup batch size starting from 1024 to 2048, and
AlphaCode, simulated programming competitions are hosted manually reduces the learning rate multiple times during the
on the Codeforces platform: overall, AlphaCode ranks at the training.
top 54.3% among over 5000 competitors, where its Codeforces Xuan Yuan 2.0 [152]: A Chinese financial chat model with
rating is within the top 28% of recently participated users. BLOOM’s [13] architecture trained on a combination of general
CodeT5+ [34]: CodeT5+ is based on CodeT5 [146], with purpose, financial, general purpose instructions, and financial
shallow encoder and deep decoder, trained in multiple stages institutions datasets. Xuan Yuan 2.0 combined the pre-training
initially unimodal data (code) and later bimodal data (text-code and fine-tuning stages to avoid catastrophic forgetting.
pairs). Each training stage has different training objectives and
activates different model blocks encoder, decoder, or both ac- 3.2. Fine-Tuned LLMs
cording to the task. The unimodal pre-training includes span
denoising and CLM objectives, whereas bimodal pre-training Pre-trained LLMs have excellent generalization abilities to
objectives contain contrastive learning, matching, and CLM for unseen tasks. However, because they are generally trained with
text-code pairs. CodeT5+ adds special tokens with the text to the objective of next token prediction, LLMs have limited ca-
enable task modes, for example, [CLS ] for contrastive loss, pacity to follow user intent and are prone to generate unethical,
[Match] for text-code matching, etc. toxic or inaccurate responses [20]. For their effective utiliza-
StarCoder [147]: A decoder-only model with the SantaCoder tion, LLMs are fine-tuned to follow instructions [16, 17, 97] and
architecture, employing Flash attention to scale up the context generate safe responses [20], which also results in increasing
length to 8k. The StarCoder trains an encoder to filter names, zero-shot, few-shot, and cross-task generalization [97, 16, 18],
with minimal compute increment, e.g., 0.2% of the total pre-
training for PaLM 540B [16].
2 https://github.com/features/copilot We review various fine-tuned LLMs and strategies for effective
3 https://codeforces.com/ fine-tuning in this section.
11
 Table 1: Noteworthy findings and insights of pre-trained Large Language Models.

Models Findings & Insights

• Encoder and decoder with shared parameters perform equivalently when parameters are not shared
T5 • Fine-tuning model layers (adapter layers) work better than the conventional way of training on only
classification layers

• Few-shot performance of LLMs is better than the zero-shot, suggesting that LLMs are meta-
GPT-3 learners

• Large multi-lingual models perform equivalently to single language models on downstream tasks.
mT5 However, smaller multi-lingual models perform worse

PanGu-α • LLMs have good few shot capabilities

• Prompt fine-tuning requires updating very few parameters while achieving performance compara-
ble to full model fine-tuning
• Prompt fine-tuning takes more time to converge as compared to full model fine-tuning
CPM-2 • Inserting prompt tokens in-between sentences can allow the model to understand relations between
sentences and long sequences
• In an analysis, CPM-2 finds that prompts work as a provider (additional context) and aggregator
(aggregate information with the input text) for the model

• A modular LLM architecture with a universal representation module and task-specific representa-
tion module helps in the finetuning phase
ERNIE 3.0 • Optimizing the parameters of a task-specific representation network during the fine-tuning phase is
an efficient way to take advantage of the powerful pre-trained model

• The performance of LLM is highly related to the network size

• To improve runtime performance, more operations can be performed in parallel (width) rather than
sequential (depth)
Jurassic-1 • To efficiently represent and fit more text in the same context length, the model uses a larger vo-
cabulary to train a SentencePiece tokenizer without restricting it to word boundaries. This further
benefits in few-shot learning tasks

• By employing prompt-based tuning, the performances of models can be improved, often surpassing
HyperCLOVA
those of state-of-the-art models when the backward gradients of inputs are accessible

• The model architecture that excels in pre-training and fine-tuning cases may exhibit contrasting
Yuan 1.0
behavior in zero-shot and few-shot learning

Gopher • Relative encodings enable the model to evaluate for longer sequences than training.

• Additional self-supervised adversarial loss to distinguish between real and generated text improves
ERNIE 3.0 Titan the model performance as compared to ERNIE 3.0

• Parallel attention + FF layers speed-up training 15% with the same performance as with cascaded
layers
GPT-NeoX-20B • Initializing feed-forward output layers before residuals with scheme in [153] avoids activations
from growing with increasing depth and width
• Training on Pile outperforms GPT-3 on five-shot
Table Continued on Next Page

12
Models Findings & Insights

• Restart training from an earlier checkpoint with a lower learning rate if loss diverges
OPT • Model is prone to generate repetitive text and stuck in a loop

• Galactica’s performance has continued to improve across validation set, in-domain, and out-of-
domain benchmarks, even with multiple repetitions of the corpus, which is superior to existing
research on LLMs
Galactica • A working memory token approach can achieve strong performance over existing methods on
mathematical MMLU and MATH benchmarks. It sets a new state-of-the-art on several downstream
tasks such as PubMedQA (77.6%) and MedMCQA dev (52.9%)

• The model capacity can be maintained at reduced computation by replacing the feed-forward layer
in each transformer layer with a mixture-of-experts (MoE)
• The model trained on filtered data shows consistently better performances on both NLG and NLU
tasks, where the effect of filtering is more significant on the former tasks
GLaM • Filtered pretraining corpora play a crucial role in the generation capability of LLMs, especially for
the downstream tasks
• The scaling of GLaM MoE models can be achieved by increasing the size or number of experts in
the MoE layer. Given a fixed budget of computation, more experts contribute to a better perfor-
mance

LaMDA • The model can be fine-tuned to learn to call different external information resources and tools

• For higher effectiveness and efficiency, a transformer model can be asymmetrically constructed
with a shallower encoder and a deeper decoder
• To achieve better performances, it is necessary to employ strategies such as massively scaling
AlphaCode upsampling, followed by the filtering and clustering of samples into a compact set
• The utilization of novel sampling-efficient transformer architectures designed to facilitate large-
scale sampling is crucial
• Simplifying problem descriptions can effectively improve the model’s performance

• The model size and the number of training tokens should be scaled proportionately: for each dou-
Chinchilla bling of the model size, the number of training tokens should be doubled as well

• English-centric models produce better translations when translating to English as compared to non-
English
• Generalized models can have equivalent performance for language translation to specialized small
PaLM models
• Larger models have a higher percentage of training data memorization
• Performance has not yet saturated even at 540B scale, which means larger models are likely to
perform better

• Encoder-decoder architecture is more suitable to train LLMs given bidirectional attention to the
context than decoder-only
AlexaTM • Causal Language Modeling (CLM) task can be added to benefit the model with efficient in-context
learning
• Placing layer norm at the beginning of each transformer layer improves the training stability
Table Continued on Next Page

13
Models Findings & Insights

• Training with a mixture of denoisers outperforms PaLM when trained further for a few more FLOPs
U-PaLM • Training with a mixture of denoisers improves the infilling ability and open-ended text generation
diversity

• Mode switching training enables better performance on downstream tasks

UL2 • CoT prompting outperforms standard prompting for UL2

• Pre-training data with a small proportion of multi-task instruction data improves the overall model
GLM-130B performance

• Multi-step prompting for code synthesis leads to a better user intent understanding and code gen-
CodeGen eration

• A constant performance improvement is observed when scaling the model

LLaMA • Smaller models can achieve good performances with more training data and computing time

• Sparse models provide the benefits of large models at a lower computation cost
• Randomly Routed Experts reduces catastrophic forgetting effects which in turn is essential for
PanGu-Σ continual learning
• Randomly Routed Experts allow extracting a domain-specific sub-model in deployment which is
cost-efficient while maintaining a performance similar to the original

• Pre-training with general-purpose and task-specific data improves task performance without hurt-
BloombergGPT ing other model capabilities

XuanYuan 2.0 • Combining pre-training and fine-tuning stages in single training avoids catastrophic forgetting

• Causal LM is crucial for a model’s generation capability in encoder-decoder architectures

CodeT5+ • Multiple training objectives like span corruption, Causal LM, matching, etc complement each other
for better performance

StarCoder • HHH prompt by Anthropic allows the model to follow instructions without fine-tuning

• Model trained on unfiltered data is more toxic but may perform better on downstream tasks after
LLaMA-2 fine-tuning
• Model trained on unfiltered data requires fewer samples for safety alignment

• Data quality is important to train better models

PaLM-2 • Model and data size should be scaled with 1:1 proportions
• Smaller models trained for larger iterations outperform larger models

• Increasing batch size gradually stabilizes the training without loss spikes
• High-quality data at the final stages of training improves the model performance
LLaMA-3/3.1 • Increasing model context length windows step-wise allows it to better adapt to various sequence
lengths

• Model aligned iteratively on synthetic data with data generated from the previously aligned model
Nemotron-40B achieves competitive performance

DeepSeek • Batch size should increase with the increase in compute budget while decreasing the learning rate

• Mult-head latent attention (MLA) performs better than multi-head attention (MHA) while requiring
DeepSeek-v2 a significantly smaller KV cache, therefore achieving faster data generation

14
 Table 2: Key insights and findings from the study of instruction-tuned Large Language Models.

Models Findings & Insights

• Multi-task prompting enables zero-shot generalization and outperforms baselines

T0 • Even a single prompt per dataset task is enough to improve performance

• To aid the model in effectively filtering and utilizing relevant information, human labelers play a
crucial role in answering questions regarding the usefulness of the retrieved documents
WebGPT • Interacting a fine-tuned language model with a text-based web-browsing environment can improve
end-to-end retrieval and synthesis via imitation learning and reinforcement learning
• Generating answers with references can make labelers easily judge the factual accuracy of answers

• Instruction tuning leads to a stronger generalization of unseen tasks

• More tasks improve generalization whereas only increasing task instances does not help
Tk-INSTRUCT • Supervised trained models are better than generalized models
• Models pre-trained with instructions and examples perform well for different types of inputs

• Instruction tuning enables zero-shot generalization to tasks never seen before

• Multi-lingual training leads to even better zero-shot generalization for both English and non-
English
mT0 and BLOOMZ • Training on machine-translated prompts improves performance for held-out tasks with non-English
prompts
• English only fine-tuning on multilingual pre-trained language model is enough to generalize to
other pre-trained language tasks

• Creating a batch with multiple task examples is important for better performance
• Only example proportional sampling is not enough, training datasets should also be proportional
for better generalization/performance
• Fully held-out and partially supervised tasks performance improves by scaling tasks or categories
OPT-IML whereas fully supervised tasks have no effect
• Including small amounts i.e. 5% of pretraining data during fine-tuning is effective
• Only 1% reasoning data improves the performance, adding more deteriorates performance
• Adding dialogue data makes the performance worse

• Labelers’ judgment and well-defined alignment rules help the model generate better responses
• Good dialogue goals can be broken down into detailed natural language rules for the agent and the
Sparrow raters
• The combination of reinforcement learning (RL) with reranking yields optimal performance in
terms of preference win rates and resilience against adversarial probing

• Finetuning with CoT improves performance on held-out tasks

• Fine-tuning along with CoT data improves reasoning abilities
• CoT tuning improves zero-shot reasoning
Flan • Performance improves with more tasks
• Instruction fine-tuning improves usability which otherwise is challenging for pre-trained models
• Improving the model’s performance with instruction tuning is compute-efficient
• Multitask prompting enables zero-shot generalization abilities in LLM

WizardCoder • Fine-tuning with re-written instruction-tuning data into a complex set improves performance

• Model learns to write safe responses with fine-tuning on safe demonstrations, while additional
LLaMA-2-Chat RLHF step further improves model safety and make it less prone to jailbreak attacks

LIMA • Less high quality data is enough for fine-tuned model generalization

15
 Figure 11: An example image shows an instance of the Flan training paradigm,
taken from [16].

P
Figure 10: This example illustrates the PanGu- architecture, as depicted in GPT-3 [6]. Contrary to this, Dynosaur [155] uses the meta-data
the image sourced from [92]. of datasets on Huggingface to prompt LLMs to generate multi-
ple task instruction-tuning datasets.
LLaMA Tuned: Various models in the literature instruction-
3.2.1. Instruction-Tuning with Manually Created Datasets tune LLaMA [156] with GPT-3 [6] or GPT-4 [157] gener-
Numerous hand-crafted instruction-tuning datasets with ated datasets. Among these, Alpaca [158], Vicuna [159],
different design choices are proposed in the literature to and LLaMA-GPT-4 [160] are a few general-purpose fine-tuned
instruction-tune LLMs. The performance of fine-tuned LLMs models, where Alpaca is trained on 52k samples from text-
depends on multiple factors, such as dataset, instruction diver- davinci-003, Vicuna on 70k samples from ShareGPT.com, and
sity, prompting templates, model size, and training objectives. LLaMA-GPT-4 by re-creating Alpaca instructions from GPT-
Keeping this in view, diverse fine-tuned models have emerged 4. Goat [161] fine-tunes LLaMA for arithmetic tasks (1 million
in the literature using manually created datasets. samples) by generating data from ChatGPT and outperforms
The models T0 [17] and mT0 (multi-lingual) [154] employ GPT-4, PaLM, BLOOM, OPT, etc., attributing its success to the
templates to convert existing datasets into prompt datasets. LLaMA’s consistent tokenization of numbers. HuaTuo [162] is
They have shown improvements in generalization to zero-shot a medical knowledge model, fine-tuned with a generated QA
and held-out tasks. Tk-Instruct [18] fine-tuned the T5 model dataset of 8k instructions.
with in-context instructions to study generalization on unseen Complex Instructions: Evol-Instruct [163, 164] prompts LLMs
tasks when given in-context instructions during test time. The to convert given instructions into a more complex set. The in-
model outperformed Instruct-GPT, despite being smaller in structions are iteratively evolved with re-writing instructions in
size, i.e., 11B parameters as compared to 175B of GPT-3. complex wording and creating new instructions. With this style
Increasing Tasks and Prompt Setups: Zero-shot and few-shot of automated instruction generation, WizardLM [163] (fine-
performance improves significantly by expanding task collec- tuned LLaMA on 250k instructions), outperforms Vicuna and
tion and prompt styles. OPT-IML [97] and Flan [16] curated Alpaca, and WizardCoder [164] (fine-tuned StarCoder) beats
larger 2k and 1.8k task datasets, respectively. While increasing Claude-Plus, Bard, and others.
task size alone is not enough, OPT-IML and Flan add more
prompting setups in their datasets, zero-shot, few-shot, and
CoT. In continuation, CoT Collection [101] fine-tunes Flan-T5 3.2.3. Aligning with Human Preferences
further on 1.88M CoT samples. Another method [102] uses
Incorporating human preferences into LLMs presents a
symbolic tasks with tasks in T0, Flan, etc.
significant advantage in mitigating undesirable behaviors and
ensuring accurate outputs. The initial work on alignment, such
as InstructGPT [20] aligns GPT-3 using a 3-step approach,
3.2.2. Instruction-Tuning with LLMs Generated Datasets instruction-tuning, reward modeling, and fine-tuning with
Generating an instruction-tuning dataset requires carefully reinforcement learning (RL). The supervised fine-tuned GPT-3
writing instructions and input-output pairs, which are often on demonstrations is queried to generate responses, which
written by humans, smaller in size, and less diverse. To over- human labelers rank according to human values, and a reward
come this, self-instruct [19] proposed an approach to prompt model is trained on the ranked data. Lastly, the GPT-3 is trained
available LLMs to generate instruction-tuning datasets. Self- with proximal policy optimization (PPO) using rewards on the
instruct outperformed models trained on manually created generated data from the reward model. LLaMA 2-Chat [21]
dataset SUPER-NATURALINSTRUCTIONS (a dataset with improves alignment by dividing reward modeling into help-
1600+ tasks) [18] by 33%. It starts with a seed of 175 tasks, 1 fulness and safety rewards and using rejection sampling in
instruction, and 1 sample per task and iteratively generates new addition to PPO. The initial four versions of LLaMA 2-Chat
instructions (52k) and instances (82k input-output pairs) using are fine-tuned with rejection sampling and then with PPO on
16
top of rejection sampling. The dataset collected through red-teaming is used to fine-tune
Aligning with Supported Evidence: This style of alignment models for safety. While red-teaming largely relies on human
allows the model to generate responses with proofs and facts, annotators, another work [180] red-team LLMs to find prompts
reduces hallucination, and assists humans more effectively, that lead to harmful outputs for other LLMs.
which increases trust in the model’s output. Similar to
the RLHF training style, a reward model is trained to rank
generated responses containing web citations in answers 3.2.4. Continue Pre-Training
to questions, which is later used to train the model, as in Although fine-tuning boosts a model’s performance, it leads
GopherCite [165], WebGPT [166], and Sparrow [167]. The to catastrophic forgetting of previously learned information.
ranking model in Sparrow [167] is divided into two branches, Concatenating fine-tuning data with a few randomly selected
preference reward and rule reward, where human annotators pre-training samples in every iteration avoids network forget-
adversarial probe the model to break a rule. These two rewards ting [181, 152]. This is also effective in adapting LLMs for
together rank a response to train with RL. cases where fine-tuning data is small and the original capac-
Aligning Directly with SFT: The PPO in the RLHF pipeline ity is to be maintained. Prompt-based continued pre-training
is complex, memory-intensive, and unstable, requiring mul- (PCP) [182] trains the model with text and instructions related
tiple models, reward, value, policy, and reference models. to tasks and then finally instruction-tunes the model for down-
Avoiding this sophisticated alignment pipeline is possible by stream tasks.
incorporating minimal changes in the supervised fine-tuning
(SFT) pipeline as in [168, 169, 170], with better or compa- 3.2.5. Sample Efficiency
rable performance to PPO. Direct preference optimization While fine-tuning data is generally many-fold smaller than
(DPO) [168] trains a model directly on the human-preferred the pre-training data, it still has to be large enough for accept-
responses to maximize the likelihood of preferred against able performance [16, 97, 18] and requires proportional com-
unpreferred responses, with per-sample importance weight. puting resources. Studying the effects on performance with less
Reward ranked fine-tuning RAFT [169] fine-tunes the model data, existing literature [183, 184] finds that models trained
on ranked responses by the reward model. Preference ranking on less data can outperform models trained with more data.
optimization (PRO) [171] and RRHF [170] penalize the model In [183], 25% of the total downstream data is found enough
to rank responses with human preferences and supervised loss. for state-of-the-art performance. Selecting coreset-based 0.5%
On the other hand, chain-of-hindsight (CoH) [172] provides of the total instruction-tuning data improves the model perfor-
feedback to the model in language rather than reward, to learn mance by 2% in [184], as compared to the complete data tun-
good versus bad responses. ing. Less is more for alignment (LIMA) [185] uses only 1000
Aligning with Synthetic Feedback: Aligning LLMs with carefully created demonstrations to fine-tune the model and has
human feedback is slow and costly. The literature suggests a achieved comparable performance to GPT-4.
semi-automated process to align LLMs by prompting LLMs to
generate helpful, honest, and ethical responses to the queries, 3.3. Increasing Context Window
and fine-tuning using the newly created dataset. Constitutional LLMs are trained with limited context windows due to ex-
AI [173] replaces human feedback in RLHF with AI, calling pensive attention and high memory requirements. A model
it RL from AI feedback (RLAIF). AlpacaFarm [174] designs trained on limited sequence lengths fails to generalize to unseen
prompts to imitate human feedback using LLMs APIs. Oppo- lengths at inference time [186, 49]. Alternatively, LLMs with
site to constitutional AI, AlpacaFarm injects noise in feedback ALiBi [65] positional encodings can perform zero-shot length
to replicate human mistakes. Self-Align [98] prompts the extrapolation. However, ALiBi has less expressive power [66]
LLM with ICL examples, instructing the LLM about what the and inferior performance on multiple benchmarks [46], and
response should contain to be considered useful and ethical. many LLMs use RoPE positional embedding that is unable to
The same LLM is later fine-tuned with the new dataset. perform zero-shot extrapolation. A larger context length has
Aligning with Prompts: LLMs can be steered with prompts to benefits such as a better understanding of longer documents,
generate desirable responses without training [175, 176]. The more samples in in-context learning, execution of bigger rea-
self-correction prompting in [176] concatenates instructions soning processes, etc. Expanding context length during fine-
and CoT with questions, guiding the model to answer its tuning is slow, inefficient, and computationally expensive [49].
instruction following a strategy to ensure moral safety before Therefore, researchers employ various context window extrap-
the actual answer. This strategy is shown to reduce the harm in olation techniques discussed below.
generated responses significantly. Position Interpolation: Rather than extrapolating, [49] shows
Red-Teaming/Jailbreaking/Adversarial Attacks: LLMs that interpolating position encodings within the pre-trained con-
exhibit harmful behaviors, hallucinations, leaking personal in- text window are more effective. The work demonstrates that
formation, and other shortcomings through adversarial probing. only 1000 steps of fine-tuning are enough to achieve better re-
The models are susceptible to generating harmful responses sults on larger windows without reducing performance com-
even though they are aligned for safety [177, 178]. Red- pared to the original context size. Giraffe [46] uses power scal-
teaming is a common approach to address illicit outputs, where ing in RoPE, and YaRN [47] proposed NTK-aware interpola-
the LLMs are prompted to generate harmful outputs [178, 179]. tion.
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Efficient Attention Mechanism: Dense global attention is
one of the major constraints in training larger context win-
dow LLMs. Using efficient attention variants, such as lo-
cal, sparse, and dilated attention, reduces the computation cost
significantly. LongT5 [48] proposes transient global atten-
tion (TGlobal), applying attention to local and global tokens
(windowed token averaging). The model replaces attention
in T5 [10] with TGlobal attention, pre-trains the model on
4098 sequence length, fine-tunes on larger window sizes, as
large as 16k, and improves task performance on longer inputs.
This shows the extrapolation ability of TGlobal attention with
only fine-tuning. COLT5 [187] uses two branches, one with
lightweight and the other with heavyweight attention and feed-
Figure 12: A flow diagram of Retrieval Augmented LLMs. The retriever ex-
forward layers. All tokens are processed from the lightweight tracts a similar context to the input and forwards it to the LLM either in simple
branch, and only important tokens are routed to the heavy- language or encoded through Fusion-in-Decoder (FiD). Depending on the task,
weight branch. LongNet [188] replaces standard attention with retrieval and generation may repeat multiple times.
dilated attention, expanding sequence length to 1 billion tokens.
LongLoRA [189] proposes shift-short attention, used during
fine-tuning to reduce dense attention costs. However, the model 3.4.1. Retrieval Augmented LLMs
during inference uses dense attention and achieves similar per- LLMs may have limited memory and outdated information,
formance as full attention fine-tuning. leading to inaccurate responses. Retrieving relevant informa-
Extrapolation without Training: LM-Infinite [186] and par- tion from external up-to-date storage enables the LLMs to
allel context windows (PCW) [190] show length extrapolation accurately answer with references and utilize more informa-
is possible using pre-trained LLMs. LM-Infinite suggested Λ- tion. With retrieval augmentation, smaller models have been
shaped attention applied within the original context window shown to perform at par with larger models. For instance, the
limits. Likewise, PCW chunks larger inputs into the pre-trained 11B model can become competitive to 540B PaLM in [25] and
context lengths and applies the same positional encodings to 7.5B to 280B Gopher in [193]. Retrieval augmented language
each chunk. modeling (RALM) has two major components, shown in
Figure 12, namely: 1) retriever and 2) language model. In
RALM, the retriever plays a crucial role in driving LLM
3.4. Augmented LLMs response, where incorrect information can steer LLMs to false
behavior. This leads to the development of various methods to
LLMs are capable of learning from the examples concate- retrieve accurate information and fuse with the query for better
nated with the input, known as context augmentation, in- performance.
context learning (ICL), or few-shot prompting. They show ex- Zero-Shot Retrieval Augmentation: This kind of augmen-
cellent generalization to unseen tasks with few-shot prompt- tation keeps the original LLM architecture and weights
ing, enabling LLMs to answer queries beyond the capacity ac- unchanged and uses BM25 [202], nearest neighbors, or frozen
quired during training [6, 55]. These emergent abilities allow pre-trained models like Bert [7] as a retriever. The retrieved
for adapting the model without fine-tuning—a costly process. information is provided as input to the model for response
Aside from this, hallucination, producing inaccurate, unsafe, generation, shown to improve performance over LLMs without
or factually incorrect responses, is common for LLMs, which is retrieval [198, 203]. In some scenarios, multiple retrieval
avoided by augmenting contextual data. While the user can pro- iterations are required to complete the task. The output
vide in-context samples in the query [54, 32], here we specifi- generated in the first iteration is forwarded to the retriever
cally refer to the methods that access external storage program- to fetch similar documents. Forward-looking active retrieval
matically, calling them augmented LLMs. (FLARE) [197] initially generates the response and corrects
The literature suggests various external memory designs to aug- the output by retrieving relevant documents if the response
ment LLMs, long-term [191, 192, 193, 194], short-term [195], contains low-confidence tokens. Similarly, RepoCoder [204]
symbolic [196], and non-symbolic [197, 198]. The memory fetches code snippets recursively for code completion.
can be maintained in different formats such as documents, vec- Training with Retrieval Augmentation: To reduce failures in
tors, or databases. A few systems maintain intermediate mem- retrieval augmentation generation (RAG), researchers train or
ory representations to retain information across multiple iter- fine-tune retrievers and LLMs with a retrieval augmentation
ations [194, 192], while others extract important information pipeline. We discuss the literature below based on their focus
from the datasets and save it in memory for recall [199]. The on the respective training processes of the pipeline.
memory read and write operations are performed either with Training LLM: Retrieval-enhanced transformer (RETRO) [193]
or without LLMs cooperation [192, 200, 194, 201], acting as shows pre-training smaller LLMs with RAG pipeline outper-
a feedback signal in [195]. We discuss different types of aug- forms larger LLMs, such as GPT-3 trained without RAG.
mented LLMs below. RETRO uses a 2-trillion token subset of MassiveText as
18
a database. The retrieval pipeline divides the input query
into subsets and retrieves relevant chunks from the database
for each subset, encoded together with input intermediate
representations for generating tokens. It uses cross-chunked
attention to attend to previous chunks auto-regressively. A
study on RETRO [205] shows models pre-trained without RAG
but fine-tuned using RAG lack the performance gains obtained
by pre-training with RAG.
Training Retriever: Quality of responses generated by LLMs
is highly dependent on the in-context examples. There-
fore, [206, 207, 208, 209] train retrievers to retrieve accurate
few-shot samples while keeping the LLM frozen for gener-
ation. Retrieved samples are ranked to build ground-truth
data to train retrievers with contrastive learning in [206, 208].
RoBERTa is trained for downstream tasks in [207] for ICL
samples retrieval. REPLUG [209] trains the retriever with
supervised signals from the frozen LLM-generated outputs.
Training Retriever and LLM: Further benefits are achieved by
training both the retriever and the model in [25, 210, 211]. In
this case, the error propagates back to the retriever, updating
Figure 13: A basic flow diagram of tool augmented LLMs. Given an input and
both the language model and the retriever. While masked a set of available tools, the model generates a plan to complete the task. The
language modeling (MLM) is a common pre-training objec- tool augmented LLMs utilize different modules iteratively, such as retriever,
tive [25, 211], retrieval pre-trained transformer (RPT) [210] tool execution, read-write to memory, feedback, etc., depending on the task.
used document chunk prediction as a pre-training objective for
long text modeling.
Encoded Context Augmentation: Concatenating retrieved and API selection steps. The API selector understands the API
documents with the query becomes infeasible as the sequence documentation to select a suitable API for the task and plan the
length and sample size grow. Encoding the context and fusing execution. ToolkenGPT [220] uses tools as tokens by concate-
it with the decoder (Fusion-in-Decoder) using cross-attention nating tool embeddings with other token embeddings. During
makes it possible to augment more samples without increasing inference, the LLM generates the tool tokens representing the
computation costs significantly [212, 193, 210, 25]. tool call, stops text generation, and restarts using the tool exe-
Web Augmented: Locally stored memory, but external to cution output.
LLM, has limited information. However, a large amount of Training with Tool Augmentation: LLMs are trained to inter-
information is available on the internet, which gets updated act with diverse tools, enhancing planning abilities to overcome
regularly. Rather than storing information locally, various the limitations of zero-shot tool augmentation [221, 27, 222,
methods retrieve query-related context through a web search 223]. Gorilla [221] instruction-tunes LLaMA with information
and forward it to LLMs [213, 214, 166]. retrieval from API documentation. It uses the self-instruct [19]
data generation pipeline with GPT-4 by providing in-context
examples retrieved from API documentation. Tool augmented
3.4.2. Tool Augmented LLMs language model (TALM) [27] fine-tunes T5 [10] for tool use
While RAG relies on the retriever to provide context to the with a self-play approach, where it iteratively completes tool
LLM to answer queries, tool augmented LLMs capitalize on the manipulation tasks and includes them back in the training set.
reasoning abilities of LLMs to iteratively plan by dividing tasks ToolLLM [223] collects 16k APIs from RapidAPI. It samples
into sub-tasks, selecting necessary tools, and taking actions to APIs from the list to generate an instruction-tuning dataset us-
complete the task [215, 216, 217, 27]. A generic pipeline of ing ChatGPT in single-tool and multi-tool scenarios. For high-
tool-augmented LLMs is shown in Figure 13, where different quality datasets, ToolLLM suggested a depth-first search-based
modules in Figure 13 are selected in a loop until the task com- decision tree (DFSDT) method to generate ground-truths with
pletion. diverse reasoning and planning.
Zero-Shot Tool Augmentation: LLMs in-context learning and Multimodal Tool Augmentation: The compositional reasoning
reasoning abilities enable them to interact with tools with- capacity of LLMs allows them to manipulate tools in multi-
out training. Automatic reasoning and tool-use (ART) [217] modal settings [215, 216, 224]. Following the pipeline shown
builds a task library with demonstrations of reasoning steps and in Figure 13, the LLM outlines a plan, generally executing in a
calling external tools. It retrieves similar task examples and sequence: Plan → Tool selection → Execute → Inspect →
provides the context to the LLM for inference. Aside from Generate, to respond to the user query. Here, the database of
this, [218] shows tool documentation is enough to teach LLMs tools is rich in modalities, including text, images, etc. Many of
to use tools without demonstrations. RestGPT [219] integrates the multimodal tool augmentation systems employ multimodal
LLMs with RESTful APIs by decomposing tasks into planning LLMs [31, 225, 224, 216], while others utilize single modality
19
LLMs and generate a plan on using different modality tools to and long-term memory, where short-term memory contains re-
solve multimodal queries [226]. cent responses and long-term memory keeps summarized failed
attempts to add in the prompt as reflection.
3.5. LLMs-Powered Agents Multi-Agents Systems: LLMs can play user-defined roles and
behave like a specific domain expert. In multi-agent systems,
AI agents are autonomous entities, capable of planning, each LLM is assigned a unique role, simulating human behav-
decision-making, and performing actions to achieve complex ior and collaborating with other agents to complete a complex
goals. In the early days, AI agents were rule-based, de- task [229, 239].
signed for narrow tasks, and had limited capabilities, such LLMs in Physical Environment: LLMs are good at
as Clippy [227] and Deep Blue [228]. In contrast to this, instruction-following, however, utilizing them for physically
LLMs abilities to respond to dynamic scenarios have made it grounded tasks requires adaptation, as they lack real-world
possible to incorporate them in diverse applications, includ- knowledge. This could lead to generating illogical responses
ing LLMs-powered agents [224, 216], where LLMs behave for a particular physical situation [240, 26]. SayCan [240]
as the brain of agents. LLMs have been incorporated in web make LLMs aware of the available low-level task operations.
agents [166, 167], coding agents [229], tool agents [27, 223], LLM (Say) builds a high-level plan to complete the task and
embodied agents [26], and conversational agents [195], requir- a learned affordance function (Can) explores the possibility of
ing minimal to no fine-tuning". Below we summarize the re- executing the plan in the real world. SayCan uses RL to train
search in LLMs-based autonomous agents. For a more detailed the language-conditioned affordance function. PaLM-E enables
discussion, please refer to [230, 231]. the LLM to solve grounded tasks by training multi-modal LLM
LLMs Steering Autonomous Agents: LLMs are the cognitive feeding inputs directly from the sensors.
controllers of the autonomous agents. They generate plans, rea- Manipulation: In the area of manipulation [236, 241], LLMs
son about tasks, incorporate memory to complete tasks, and enhance a robot’s dexterity and adaptability, excelling in tasks
adapt the outline depending on the feedback from the environ- like object recognition, grasping, and collaboration. They ana-
ment. Depending on the acquired capabilities of LLMs, many lyze visual and spatial information to determine the most effec-
methods fine-tune, propose a better prompting approach, or uti- tive approach to interact with objects.
lize different modules to enhance agents’ performance. Mod- Navigation: LLMs enhance a robot’s ability to navigate com-
ules and strategies employed in autonomous agents are briefly plex environments with precision and adaptability [242, 243,
discussed below. 244, 245]. They generate feasible paths and trajectories for
Planning and Reasoning: Completing a complex task requires robots, accounting for intricate environmental details [246].
human-like logical thinking, planning necessary steps, and This ability is valuable in scenarios requiring precise and
reasoning current and future directions. Prompting methods dynamically adaptable navigation in environments like ware-
like chain-of-thoughts [103], tree-of-thoughts [105], and self- houses, transport, healthcare facilities, and residences.
consistency [104] are central to agents, eliciting LLMs to rea-
son its actions and choose among different paths for task com- 3.6. Efficient LLMs
pletion. When LLMs are prompted with a task description and
a sequence of actions, they can accurately generate plan ac- Deploying LLMs in production is expensive. Reducing their
tions without any fine-tuning [232]. Reasoning via planning running costs while preserving performance is an appealing
(RAP) [233] incorporates a re-purposed LLM as a world model area of research. This section summarizes the approaches sug-
to reason about future outcomes and explore alternative paths gested to enhance LLMs’ efficiency.
for task completion. Retroformer [234] uses a retrospective
LLM to improve main LLM planning and reasoning capabil- 3.6.1. Parameter Efficient Fine-Tuning
ities by providing helpful task cues. Fine-tuning LLMs with tens or hundreds of billions of pa-
Feedback: LLMs in open-loop systems generate plans and as- rameters, such as GPT-3 (175B), BLOOM (176B), MT-NLG
sume that the agent will complete them successfully. However, (540B), etc., is computationally intensive and time-consuming.
the actual scenario is different with failures and variable re- To avoid complete model fine-tuning, numerous parameter-
sponses from the environment. To correctly complete tasks, efficient fine-tuning (PEFT) techniques [40, 247, 41, 38, 39] try
many methods use LLMs in a closed-loop where the action re- to achieve acceptable model fine-tuning performance at reduced
sponse is provided as feedback to the LLMs to re-assess and costs. As compared to full fine-tuning [248], PEFT performs
update the plan as required [235, 236, 237, 195]. Another di- better in low-resource setups, achieves comparable perfor-
rection of research exploits LLMs as reward functions to train mance on medium-resource scenarios, and performs worse than
reinforcement learning (RL) policies instead of humans [238]. full fine-tuning under high-resource availability. An overview
Memory: LLMs can learn from the context provided in the of different PEFT approaches is shown in Figure 14.
prompt. In addition to internal memory, various systems em- Adapter Tuning: Adds a few trainable parameters within the
ploy external memory to save the response history. Reflex- transformer block. The adapter layer is a sequence of feature
ion [195] maintains an episodic memory to use previous re- downscaling, non-linearity, and upscaling [106]. Variants of
sponses as feedback to improve future decision-making. Retro- adapter tuning inject adapter layers sequentially [106] and in
former [234] improves its responses by employing short-term parallel [38], whereas the mixture of adapter (AdaMix) [249]
20
Figure 14: Illustration of parameter-efficient fine-tuning paradigms, where x is input and h is hidden state, figure courtesy [38]. Parallel adapter and LoRA fall in
the adapter tuning category.

employs multiple adapter modules in a single layer. AdaMix FP16 format [44]. Such demanding requirements for deploying
routes input instances randomly to one of the multiple down- LLMs make it harder for smaller organizations to utilize them.
scale and upscale modules. The mixture of adapters is averaged Model compression is an effective solution but comes at the cost
out for inference to avoid additional latency. Low-Rank Adap- of degraded performance, especially at large scales greater than
tation (LoRA) [250] learns low-rank decomposed matrices to 6B. These models exhibit very large magnitude outliers that do
freeze original weights. The learned weights are fused with the not exist in smaller models [255], making it challenging and re-
original weights for inference, avoiding latency. quiring specialized methods for quantizing LLMs [44, 256].
Prompt Tuning: Prompting is an effective way to adapt a Post-Training Quantization: Minimal or no training is re-
pre-trained LLM for the downstream task. However, manual quired in this type of quantization, without significantly com-
prompts bring uncertainty in the model’s prediction, where a promising the model performance. LLM-8-bit [255] uses full-
change in a single word drops the performance [247]. Prompt precision matrix multiplication for weights associated with out-
tuning alleviates this problem by fine-tuning only 0.001%-3% lier features and 8-bit for remaining features. The lower pre-
additional parameters [251]. It concatenates trainable prompt cision multiplication outputs are converted to FP-16 and con-
parameters with the model embeddings [247, 40, 251]. Task- catenated with others. The quantized models have homogenous
specific fixed discrete prompts are concatenated with input em- word embeddings, which may degrade their performance. To
beddings in [40]. As discrete prompts bring instability, prompts fix this, token-level knowledge distillation is employed in [45]
are encoded through a learnable mapping in P-Tuning [247], along with independent quantization scaling factors for each
naming continuous prompts, which are appended with the dis- module due to varying weight distribution. Feature distribu-
crete prompts. Only the prompt encoder is trainable in the tions are asymmetric and appear in different channels; outlier
model. In an extension of P-Tuning, continuous prompts are suppression [257] shifts and scales per-channel activation dis-
concatenated with each layer of the network in [251]. Progres- tributions for effective quantization. SmoothQuant [44] quan-
sive prompts [252] avoid catastrophic forgetting and transfer tizes activations and weights to INT8 format by smoothing
previously learned knowledge by sequentially adding trainable activations and migrating the quantization difficulty toward
prompt embeddings to the previously frozen task embeddings. weights. It multiplies the inverse of the smoothing factor with
Prefix Tuning: A set of trainable task-specific prefix vectors weights, which introduces a few outliers in the weights but is
are appended to the frozen transformer layers in prefix tun- easier to quantify than unsmoothed activations. OPTQ [256]
ing [41]. The prefix vectors are virtual tokens attended by the uses the optimal brain compression (OBC) [258] algorithm to
context tokens on the right. In addition, adaptive prefix tun- quantize the model layer-by-layer and update weights to com-
ing [253] applies a gating mechanism to control the information pensate for quantization error. To improve speed and per-
from the prefix and actual tokens. formance, OPTQ updates weights in arbitrary order, employs
Bias Tuning: Fine-tuning only bias terms in small to medium lazy updates, and uses better Cholesky kernels. Outlier-aware
training data has been found effective in BitFit [254]. This weight quantization (OWQ) [259] uses the OPTQ algorithm for
method achieves full fine-tuning performance for tasks with less quantization but assigns higher precision to vulnerable weights,
training data and comparable performance with more training causing outliers and lower precision for others.
data. Quantization-Aware Training: To compensate for perfor-
mance degradation, a quantized model is fine-tuned in
quantization-aware training (QAT) [260, 261, 262]. Al-
3.6.2. Quantization
pha Tuning quantizes the model using binary coding quan-
LLMs require extensive computing and memory for infer-
tization (BCQ) [263] and fine-tunes only quantization scal-
ence. Deploying a 175B parameter GPT-3 model needs at
ing factors. This approach improves performance over
least five 80GB A100 GPUs and 350GB of memory to store in
21
parameter-efficient fine-tuning of the pre-trained model. Sim- now facilitating LLMs to perceive different modalities of infor-
ilarly, parameter-efficient and quantization-aware adaptation mation like image [269, 270, 271], video [272, 273, 274], au-
(PEQA) [264] reduces the precision of fully-connected layers dio [275, 274, 276], etc. Multimodal LLMs (MLLMs) present
and fine-tunes only quantization scaling parameters. LLM- substantial benefits compared to standard LLMs that process
QAT [262] generates training data from the pre-trained network only text. By incorporating information from various modal-
and trains a quantized student model with knowledge distilla- ities, MLLMs can achieve a deeper understanding of context,
tion. QLoRA [261] fine-tunes 4-bit quantized pre-trained LLM leading to more intelligent responses infused with a variety of
with LoRA [250] using a 4-bit normal float, which shows better expressions. Importantly, MLLMs align closely with human
performance over a 4-bit integer and float. perceptual experiences, leveraging the synergistic nature of our
multisensory inputs to form a comprehensive understanding of
3.6.3. Pruning the world [276, 26]. Coupled with a user-friendly interface,
Pruning is an alternative approach to quantization to com- MLLMs can offer intuitive, flexible, and adaptable interactions,
press model size, thereby reducing LLMs deployment costs allowing users to engage with intelligent assistants through a
significantly. Compared to task-agnostic pruning, task-specific spectrum of input methods. According to the ways of construct-
pruning is easily achievable with good performance, where a ing models, current MLLMs can be generally divided into three
model is fine-tuned on the downstream task and pruned for streams: pre-training, fine-tuning, and prompting. In this sec-
faster inference. It is possible to prune LLMs for individual tion, we will discuss more details of these main streams, as well
tasks, but the cost of pruning and deploying task-specific mod- as the important application of MLLMs in visual reasoning.
els is high. To overcome this, many structured and unstructured Pre-training: This stream of MLLMs intends to support differ-
pruning methods for LLMs have been proposed to maintain rea- ent modalities using unified end-to-end models. For instance,
sonable performance across all tasks while shrinking the model Flamingo [269] applies gated cross-attention to fuse vision and
size [265, 42, 266]. language modalities, which are collected from pre-trained and
Unstructured Pruning: This kind of pruning removes less im- frozen visual encoder and LLM, respectively. Moreover, BLIP-
portant weights without maintaining any structure. Existing 2 [270] proposes a two-stage strategy to pre-train a Querying
LLM pruning methods take advantage of the unique charac- Transformer (Q-Former) for the alignment between vision and
teristics of LLMs, uncommon for smaller models, where a language modalities: in the first stage, vision-language repre-
small subset of hidden states are activated with large magni- sentation learning is bootstrapped from a frozen visual encoder;
tude [255]. Pruning by weights and activations (Wanda) [265] and in the second stage, a frozen LLM bootstraps vision-to-
prunes weights in every row based on importance, calculated language generative learning for zero-shot image-to-text gen-
by multiplying the weights with the norm of input. The pruned eration. Similarly, MiniGPT-4 [277] deploys pre-trained and
model does not require fine-tuning, thereby saving computa- frozen ViT [278], Q-Former and Vicuna LLM [159], only train-
tional costs. Outlier weighed layerwise sparsity (OWL) [267] ing the linear projection layer for vision and language modali-
extends Wanda with non-uniform layer pruning. It shows that ties alignment.
the number of outliers varies for different layers; therefore, the Fine-tuning: Derived from instruction tuning [16] for NLP
model should have variable pruning ratios for better perfor- tasks [20, 16, 97], researchers are fine-tune pre-trained LLMs
mance for every layer. Contrastive pruning (CAP) [43] itera- using multimodal instructions. Following this method, LLMs
tively prunes the model by training the sparse model using con- can be easily and effectively extended as multimodal chat-
trastive loss between pre-trained, fine-tuned, and snapshots of bots [277, 271, 29] and multimodal task solvers [279, 30, 280].
previous sparse models to learn task-specific and task-agnostic The key issue of this stream of MLLMs is to collect multi-
knowledge. modal instruction-following data for fine-tuning [58]. To ad-
Structured Pruning: Here, the parameters are removed in dress this issue, the solutions of benchmark adaptation [279,
groups, rows, columns, or matrices, which speeds up the 281, 282], self-instruction [19, 31, 283], and hybrid composi-
inference because of effective hardware tensor core utiliza- tion [284, 280] are employed, respectively. To mitigate the gap
tion [265]. LLM-Pruner [42] employs a 3-stage structured between the original language modality and additional modal-
pruning strategy, identifying the groups of hidden states caus- ities, the learnable interface is introduced to connect differ-
ing each other to activate during the forward-pass, keeping im- ent modalities from frozen pre-trained models. Particularly,
portant groups and removing less important ones, and fine- the learnable interface is expected to work in a parameter-
tuning the pruned model with LoRA. Sparsity-induced mask efficient tuning manner: e.g., LLaMA-Adapter [285] applies
learning (SIMPLE) [268] prunes the network using learnable an efficient transformer-based adapter module for training,
masks. Similarly, another method prunes LLMs by learning and LaVIN [284] dynamically learns the multimodal feature
masks and removing unimportant rank-1 components of the weights using a mixture-of-modality adapter. Different from
factorized weight matrix [266]. the learnable interface, the expert models can directly convert
multimodalities into language: e.g., VideoChat-Text [272] in-
corporates Whisper [286], a speech recognition expert model,
3.7. Multimodal LLMs
to generate the captions of given videos for the understanding
Inspired by the success of LLMs in natural language process- of following LLMs.
ing applications, an increasing number of research works are Prompting: Different from the fine-tuning technique that
22
directly updates the model parameters given task-specific positional encoding also affects the performance and training
datasets, the prompting technique provides certain context, ex- stability of LLMs. BLOOM [13] finds ALiBi outperforms
amples, or instructions to the model, fulfilling specialized tasks learned and rotary positional encodings. Contrary to this,
without changing the model parameters. Since prompting can GLM-130B [33] identifies rotary positional encoding as being
significantly reduce the need for large-scale multimodal data, better than ALiBi. So, there is no conclusion in the literature
this technique is widely used to construct MLLMs. Particularly, about positional encodings yet.
to solve multimodal Chain of Thought (CoT) problems [103], Parallel Attention: In this type of attention, feed-forward and
LLMs are prompted to generate both the reasoning process and attention layers are parallel to each other rather than sequen-
the answer given multimodal inputs [287]. On this front, differ- tial in a transformer block. It has been shown to reduce train-
ent learning paradigms are exploited in practice: for example, ing time by 15%. There is no evidence of performance drop
Multimodal-CoT [287] involves two stages of rationale genera- due to this change in the literature and it is used by the models
tion and answer inference, where the input of the second stage PaLM [15], GPT-NeoX [118], and CodeGen [140].
is a combination of the original input and the output of the first Multi-Query Attention It has shared key and value attention
stage; and CoT-PT [288] applies both prompt tuning and spe- heads in a transformer block while query attention heads are
cific visual bias to generate a chain of reasoning implicitly. In projected as usual. This reduces memory usage and speeds up
addition to CoT problems, LLMs can also be prompted with sampling in autoregressive decoding. No performance degrada-
multimodal descriptions and tools, effectively dividing complex tion has been observed with this change and it makes the train-
tasks into sub-tasks [289, 290]. ing efficient allowing larger batch sizes. Multi-query attention
Visual Reasoning Application: Recent visual reasoning sys- is used in [15, 142].
tems [291, 292, 216, 293] tend to apply LLMs for better visual Mixture of Experts: This type of architecture enables eas-
information analysis and visual-language integration. Differ- ily scaling models to trillions of parameters [92, 91]. Only a
ent from previous works [294, 295] that rely on limited VQA few experts are activated during the computation making them
datasets and small-scale neural networks, current LLM-aided compute-efficient. The performance of MoE models is better
methods offer benefits of stronger generalization ability, emer- than dense models for the same amount of data and requires less
gent ability, and interactivity [58]. To realize visual reasoning computation during fine-tuning to achieve performance similar
with the help of LLMs, prompting and fine-tuning techniques to dense models as discussed in [91]. MoE architectures are
can also be utilized: for example, PointClip V2 [292] applies less prone to catastrophic forgetting, therefore are more suited
LLMs to generate 3D-specific prompts, which are encoded as for continual learning [92]. Extracting smaller sub-models for
textual features and then combined with visual features for downstream tasks is possible without losing any performance,
3D recognition; and GPT4Tools [31] employs LoRA [250] to making MoE architecture hardware-friendly [92].
fine-tune LLMs following tool-related instructions. Serving Sparse vs Dense Activated: GPT-3 [6] uses sparse transform-
P
as a controller [293], decision maker [296], or semantics re- ers [67] whereas GLaM [91] and PanGu- [92] use MoE [121]
finer [291, 297], LLMs significantly facilitates the progress of architectures to lower computational costs and increase the
visual reasoning research. model size and capacity. According to the literature, sparse
modules do not degrade the model’s performance [67]. How-
3.8. Summary and Discussion ever, more experiments are required to verify this statement.

3.8.1. Architecture 3.8.2. Training Strategies

Due to the gigantic scale of LLMs, minor changes in archi- Training models at a huge scale require tricks to reduce train-
tecture and training strategies have a big impact on performance ing costs, avoid loss divergence, and achieve better perfor-
and stability. Here, we summarize key architectural modules mance. We summarize and discuss some of these key tricks
used in various LLMs, leading to better performance, reduced used in different LLMs.
training time and memory, and better training stability. Mixed Precision: It is a famous method for LLMs to reduce
Layer Normalization: The performance and training stability memory usage and improve training efficiency. In mixed pre-
of LLMs are affected significantly by layer normalization. Pre- cision, forward and backward passes are performed in FP16
norm, that is normalizing inputs rather than outputs, is more format whereas optimizer states and master weights are kept
common among LLMs stabilizing the training [6, 127, 108]. in FP32 format [120]. A drawback associated with this for-
BLOOM [13] and AlexaTM [122] utilize an additional layer mat change is training instability due to a smaller value range
normalization before embedding layer to stabilize the training resulting in loss spikes [33]. An alternative to FP16 is BF16
of large-scale models, while the model’s zero-shot generaliza- which has a comparatively larger range and performs precision-
tion ability can be negatively impacted [13]. However, another sensitive operations like gradient accumulation and softmax in
study [33] finds that pre-norm degrades fine-tuned model per- FP32 [13]. BF16 has better performance and training stability
formance as compared to post-norm, and there are no stability but uses more memory and is supported on specific hardware,
benefits of pre-norm beyond the 100B scale. Therefore, GLM- for example, A100 GPUs. Therefore, its adoption in LLMs is
130B [33] used deep-norm which is a variant of post-norm for limited.
better downstream task performance after fine-tuning. Training Instability: Loss divergence or spiking is a common
Positional Encoding: Like other building blocks of the model, issue in LLMs that occurs multiple times during training. This
23
happens in the presence of gradient clipping [15]. To mitigate 3.8.4. Zero-Shot vs Few-Shot
this problem, many approaches suggest restarting training from LLMs perform well in zero-shot and few-shot settings. But
an earlier checkpoint [15, 33, 91], skipping 200-500 earlier the performance difference between zero-shot and few-shot is
data batches at the point of divergence in [15] and re-shuffling large for pre-trained models [6, 15], naming LLMs as meta-
batches in [91]. The embedding layer gradient shrink proves to learners [6]. LLMs zero-shot evaluations underperform unsu-
further stabilize the training as its gradient norm is significantly pervised methods in neural machine translation [6]. The liter-
larger than the other layers [33]. Another suggestion to improve ature shows pre-training is not enough for good zero-shot per-
training stability for larger models is not to use biases in dense formance [15, 16]. To improve the zero-shot performance the
and norm layers as in [15]. literature suggests using instruction fine-tuning that improves
Weight Initialization: It plays a significant role in model con- the zero-shot performance significantly and outperforms base-
vergence and training stability. GPT-NeoX [118] initializes lines. Instruction fine-tuning has also been shown to improve
feed-forward layers before residuals with L 2√d as in [153] and zero-shot generalization to unseen tasks. Another model, Flan-
other layers with the small initialization scheme [298]. This PaLM [16], unlocks zero-shot reasoning with CoT training.
avoids activations growing exponentially with increasing depth.
MT-NLG [117] found higher variance for weight initialization 3.8.5. Encoder vs Decoder vs Encoder-Decoder
leads to unstable training, hence validating small initialization Traditionally, these architectures perform well for different
scheme [298]. Various models perform random weight initial- tasks, for example, encoder-only for NLU tasks, decoder-only
ization which can cause bad initialization, Galactica [148] sug- for NLG, and encoder-decoder for sequence2sequence model-
gests a longer warmup to negate the effect. ing. Encoder-only models are famous for smaller models such
Learning Rate: A suitable learning rate is important for sta- as Bert [7], RoBERTa [299], etc., whereas LLMs are either
ble training. It is suggested to use a lower value [13, 15, 124] decoder-only [6, 118, 13] or encoder-decoder [10, 11, 122].
with warmup and decay (cosine or linear). Usually, the learn- While decoder-only models are good at NLG tasks, various
ing rate is within the range 1e−4 to 8e−4 . Moreover, MT-NLG LLMs, PaLM [15], OPT [14], GPT-3 [6], BLOOM [13],
(530B) [117] and GPT-NeoX (20B) [118] suggest interpolat- LLaMA [156], are decoder-only models with significant per-
ing learning rates based on the model size using the GPT-3 [6] formance gains on both NLU and NLG tasks. In contradic-
models ranging between 13B and 175B. This avoids tuning the tion to this, T5 [10] and UL2 [125] identify encoder-decoder
learning rate hyperparameter. models out-performing decoder-only models. In another study,
Training Parallelism: 3D parallelism, a combination of data, PaLM [15] finds increasing the size of decoder-only models
pipeline, and tensor parallelism, is the most utilized training can reduce the performance gap between decoder-only and
parallelism approach in LLMs [33, 15, 14, 13, 117, 115, 112]. encoder-decoder architectures.
In addition to 3D parallelism, BLOOM [13] uses a zero op- Although decoder-only architectures have become a trend for
timizer [37] to shard optimizer states. PanGu-α [108] and LLMs, many recently proposed approaches [125, 122] use
PanGu-Σ [92] go beyond 3D parallelism and apply 5D paral- mode-switching tokens in text with encoder-decoder architec-
lelism which additionally contains optimizer parallelism and tures to enable task-specific modes. Similarly, CodeT5+ [34]
rematerialization. uses an encoder-decoder architecture with multiple training ob-
Mode Switching: It adds task-related tokens at the beginning jectives for different tasks, activating the encoder, decoder, or
of the text during training. These tokens refer to the natural both according to the tasks. These variations in architecture
language understanding and natural language generation tasks and training objectives allow a model to perform well in differ-
which are shown to improve downstream task performance ent settings. Because of this dynamic configuration, the future
in [125, 124, 122]. During fine-tuning and inference, tokens of LLMs can be attributed to encoder-decoder architectures.
are appended based on the downstream tasks.
Controllable Text Generation: Generating credible and con- 4. Model Configurations
trolled text from a pre-trained model is challenging. GPT-3 [6]
and other LLMs use in-context learning to control generated We provide different statistics of pre-trained and instruction-
text. While in-context learning helps in controlling the gener- tuned models in this section. This includes information such as
ated text, ERNIE 3.0 Titan [35] suggests using adversarial loss publication venue, license type, model creators, steps trained,
to rank its generated text for credibility and soft prompts such as parallelism, etc in Table 3 and Table 4. Architecture details
genre, topic, keywords, sentiment, and length for better control of pre-trained LLMs are available in Table 5. Providing these
on generated text. details for instruction-tuned models is unnecessary because it
fine-tunes pre-trained models for instruction datasets. Hence,
architectural details are the same as the baselines. Moreover,
3.8.3. Supervised Models vs Generalized Models optimization settings for various LLMs are available in Table 6
Although generalized models are capable of performing di- and Table 7. We do not include details on precision, warmup,
verse tasks with good performance they have not yet outper- and weight decay in Table 7. These details are not as important
formed models trained in supervised settings. The supervised as others to mention for instruction-tuned models, and are not
trained models are still state-of-the-art in various NLP tasks by provided by the papers.
a large margin as shown in [6, 15, 18].
24
Table 3: Summary of pre-trained LLMs (>10B). Only the LLMs discussed individually in the previous sections are summarized. “Data/Tokens” is the model’s
pre-training data, which is either the number of tokens or data size. “Data Cleaning” indicates whether data cleaning is performed or not. This includes heuristics
(Heur), deduplication (Dedup), quality filtering (QF), and privacy filtering (PF), “Cost” is the calculated training cost obtained by multiplying the GPUs/TPUs
hourly rate with the number of GPUs and the training time. The actual cost may vary due to many reasons such as using in-house GPUs or getting a discounted rate,
re-training, number of employees working on the problem, etc. “Training Parallelism” indicates distributed training using data parallelism (D), tensor parallelism
(T), pipeline parallelism (P), context parallelism (C), model parallelism (M), optimizer parallelism (OP), and rematerialization (R), where for “Library” column,
“DS” is a short form for Deep Speed. In column “Commercial Use”, we assumed a model is for non-commercial purposes if its license is unavailable.

Publication License Model No. of Commercial Steps Data/ Data No. of Processing Training Calculated Training
Models
Venue Type Creators Purpose Params Use Trained Tokens Cleaning Processing Units Unit Type Time Train. Cost Parallelism Library
T5 [10] JMLR'20 Apache-2.0 Google General 11B ✓ 1M 1T Heur+Dedup 1024 TPU v3 - - D+M Mesh TensorFlow
GPT-3 [6] NeurIPS'20 - OpenAI General 175B × - 300B Dedup+QF - V100 - - M -
mT5 [11] NAACL'21 Apache-2.0 Google General 13B ✓ 1M 1T - - - - - - -
PanGu-α [108] arXiv'21 Apache-2.0 Huawei General 200B ✓ 260k 1.1TB Heur+Dedup 2048 Ascend 910 - - D+OP+P+O+R MindSpore
CPM-2 [12] AI Open'21 MIT Tsinghua General 198B ✓ 1M 2.6TB Dedup - - - - D+M JAXFormer
Codex [141] arXiv'21 - OpenAI Coding 12B × - 100B Heur - - - - - -
ERNIE 3.0 [110] arXiv'21 - Baidu General 10B × 120k∗ 375B Heur+Dedup 384 V100 - - M∗ PaddlePaddle
Jurassic-1 [112] White-Paper'21 Apache-2.0 AI21 General 178B ✓ - 300B - 800 GPU - - D+M+P Megatron+DS
HyperCLOVA [114] EMNLP'21 - Naver General 82B × - 300B Clf+Dedup+PF 1024 A100 321h 1.32 Mil M Megatron
Yuan 1.0 [115] arXiv'21 Apache-2.0 - General 245B ✓ 26k∗ 180B Heur+Clf+Dedup 2128 GPU - - D+T+P -
Gopher [116] arXiv'21 - Google General 280B × - 300B QF+Dedup 4096 TPU v3 920h 13.19 Mil D+M JAX+Haiku
ERNIE 3.0 Titan [35] arXiv'21 - Baidu General 260B × - 300B Heur+Dedup - Ascend 910 - - D+M+P+D* PaddlePaddle
GPT-NeoX-20B [118] BigScience'22 Apache-2.0 EleutherAI General 20B ✓ 150k 825GB None 96 40G A100 - - M Megatron+DS+PyTorch
OPT [14] arXiv'22 MIT Meta General 175B ✓ 150k 180B Dedup 992 80G A100 - - D+T Megatron
BLOOM [13] arXiv'22 RAIL-1.0 BigScience General 176B ✓ - 366B Dedup+PR 384 80G A100 2520h 3.87 Mil D+T+P Megatron+DS
Galactica [148] arXiv'22 Apache-2.0 Meta Science 120B × 225k 106B Dedup 128 80GB A100 - - - Metaseq
GLaM [91] ICML'22 - Google General 1.2T × 600k∗ 600B Clf 1024 TPU v4 - - M GSPMD
LaMDA [150] arXiv'22 - Google Dialog 137B × 3M 2.81T Filtered 1024 TPU v3 1384h 4.96 Mil D+M Lingvo
MT-NLG [117] arXiv'22 Apache-v2.0 MS.+Nvidia General 530B × - 270B - 4480 80G A100 - - D+T+P Megatron+DS
AlphaCode [142] Science'22 Apache-v2.0 Google Coding 41B ✓ 205k 967B Heur+Dedup - TPU v4 - - M JAX+Haiku
Chinchilla [96] arXiv'22 - Google General 70B × - 1.4T QF+Dedup - TPUv4 - - - JAX+Haiku
PaLM [15] arXiv'22 - Google General 540B × 255k 780B Heur 6144 TPU v4 - - D+M JAX+T5X
AlexaTM [122] arXiv'22 Apache v2.0 Amazon General 20B × 500k 1.1T Filtered 128 A100 2880h 1.47 Mil M DS
U-PaLM [124] arXiv'22 - Google General 540B × 20k - - 512 TPU v4 120h 0.25 Mil - -
UL2 [125] ICLR'23 Apache-2.0 Google General 20B ✓ 2M 1T - 512 TPU v4 - - M JAX+T5X
GLM [33] ICLR'23 Apache-2.0 Multiple General 130B × - 400B - 768 40G A100 1440h 3.37 Mil M -
CodeGen [140] ICLR'23 Apache-2.0 Salesforce Coding 16B ✓ 650k 577B Heur+Dedup - TPU v4 - - D+M JAXFormer
LLaMA [127] arXiv'23 - Meta General 65B × 350k 1.4T Clf+Heur+Dedup 2048 80G A100 504h 4.12 Mil D+M xFormers
PanGuΣ [92] arXiv'23 - Huawei General 1.085T × - 329B - 512 Ascend 910 2400h - D+OP+P+O+R MindSpore
BloombergGPT [151] arXiv23 - Bloomberg Finance 50B × 139k 569B Dedup 512 40G A100 1272h 1.97 Mil M PyTorch
Xuan Yuan 2.0 [152] arXiv23 RAIL-1.0 Du Xiaoman Finance 176B ✓ - 366B Filtered - 80GB A100 - - P DS
CodeT5+ [34] arXiv'23 BSD-3 Salesforce Coding 16B ✓ 110k 51.5B Dedup 16 40G A100 - - - DS
StarCoder [147] arXiv'23 OpenRAIL-M BigCode Coding 15.5B ✓ 250k 1T Dedup+QF+PF 512 80G A100 624h 1.28 Mil D+T+P Megatron-LM
LLaMA-2 [21] arXiv'23 LLaMA-2.0 Meta General 70B ✓ 500k 2T Minimal Filtering - 80G A100 1.7Mh - - -
PaLM-2 [123] arXiv'23 - Google General - × - - Ddedup+PF+QF - - - - - -
LLaMA-3.1 [130] arXiv'24 LLaMA-3.0 Meta General 405B ✓ 1.2M 15T Dedup+QF 16k 80G H100 30.84Mh - D+T+P+C PyTorch
Mixtral 8x22B [131] web'24 Apache-2.0 Mistral AI General 141B ✓ - - - - - - - - -
Snowflake Arctic [132] web'24 Apache-2.0 Snowflake General 480B ✓ - 3.5T - - - - T+P DS
Nemotron-4 340B [137]web'24 Nvidia Nvidia General 340B ✓ - 9T - 6144 80G H100 - - D+T+P -
DeepSeek [138] arXiv'24 MIT DeepSeek General 67B ✓ - 2T Dedup+QF - - 300.6Kh - D+T+P DS
DeepSeek-v2 [139] arXiv'24 MIT DeepSeek General 67B ✓ - 8.1T QF - H800 172.8Kh - D+P HAI-LLM

Table 4: Summary of instruction tuned LLMs (>10B). All abbreviations are the same as Table 3. Entries in “Data/Tokens” starting with “S-” represent the number
of training samples.

Publication License Model No. of Commercial Pre-trained Steps Data/ No. of Processing Train. Calculated Train.
Models
Venue Type Creators Purpose Params Use Models Trained Tokens Processing Units Unit Type Time Train. Cost Parallelism Library
WebGPT [166] arXiv'21 - OpenAI General 175B × GPT-3 - - - - - - - -
T0 [17] ICLR'22 Apache-2.0 BigScience General 11B ✓ T5 - 250B 512 TPU v3 270h 0.48 Mil - -
Tk-Instruct [18] EMNLP'22 MIT AI2+ General 11B ✓ T5 1000 - 256 TPU v3 4h 0.0036 Mil - Google T5
OPT-IML [97] arXiv'22 - Meta General 175B × OPT 8k 2B 128 40G A100 - - D+T Megatron
Flan-U-PaLM [16] ICLR'22 Apache-2.0 Google General 540B ✓ U-PaLM 30k - 512 TPU v4 - - - JAX+T5X
mT0 [154] ACL'23 Apache-2.0 HuggingFace+ General 13B ✓ mT5 - - - - - - - -
Sparrow [167] arXiv'22 - Google Dialog 70B × Chinchilla - - 64 TPU v3 - - M -
WizardCoder [164] arXiv'23 Apache-2.0 HK Bapt. Coding 15B × StarCoder 200 S-78k - - - - - -
Alpaca [158] Github'23 Apache-2.0 Stanford General 13B ✓ LLaMA 3-Epoch S-52k 8 80G A100 3h 600 FSDP PyTorch
Vicuna [159] Github'23 Apache-2.0 LMSYS General 13B ✓ LLaMA 3-Epoch S-125k - - - - FSDP PyTorch
LIMA [185] arXiv'23 - Meta+ General 65B - LLaMA 15-Epoch S-1000 - - - - - -
Koala [300] Github'23 Apache-2.0 UC-Berkley General 13B × LLaMA 2-Epoch S-472k 8 A100 6h 100 - JAX/FLAX

5. Datasets and Evaluation 5.1. Training Datasets

The performance of LLMs largely depends on the training

data’s quality, size, and diversity. Preparing training datasets
Generating training and evaluation datasets is expensive be- of high quality at a large scale is laborious. Researchers have
cause of the large-scale data demand of LLMs. Hence, datasets suggested various pre-training and fine-tuning datasets to en-
for training and benchmarking these models are topics of key hance LLMs capabilities. We summarize these efforts in Ta-
importance. A summary of datasets commonly used by LLMs ble 8. While numerous training datasets are available in the
is provided next. literature, we cover the most widely used ones in our summary.
25
Table 5: Architecture details of LLMs. Here, “PE” is the positional embedding, “nL” is the number of layers, “nH” is the number of attention heads, “HS” is the
size of hidden states.

Training
Models Type Attention Vocab Tokenizer Norm PE Activation Bias nL nH HS
Objective
T5 (11B) Enc-Dec Span Corruption Standard 32k SentencePiece Pre-RMS Relative ReLU × 24 128 1024
GPT3 (175B) Causal-Dec Next Token Dense+Sparse - - Layer Learned GeLU ✓ 96 96 12288
mT5 (13B) Enc-Dec Span Corruption Standard 250k SentencePiece Pre-RMS Relative ReLU - - - -
PanGu-α (200B) Causal-Dec Next Token Standard 40k BPE Layer - - - 64 128 16384
CPM-2 (198B) Enc-Dec Span Corruption Standard 250k SentencePiece Pre-RMS Relative ReLU - 24 64 -
Codex (12B) Causal-Dec Next Token Standard - BPE+ Pre-Layer Learned GeLU - 96 96 12288
ERNIE 3.0 (10B) Causal-Dec Next Token Standard - WordPiece Post-Layer Relative GeLU - 48 64 4096
Jurassic-1 (178B) Causal-Dec Next Token Standard 256k SentencePiece∗ Pre-Layer Learned GeLU ✓ 76 96 13824
HyperCLOVA (82B) Causal-Dec Next Token Dense+Sparse - BPE* Pre-Layer Learned GeLU - 64 80 10240
Yuan 1.0 (245B) Causal-Dec Next Token Standard - - - - - - 76 - 16384
Gopher (280B) Causal-Dec Next Token Standard 32k SentencePiece Pre-RMS Relative GeLU ✓ 80 128 16384
ERNIE 3.0 Titan (260B) Causal-Dec Next Token Standard - WordPiece Post-Layer Relative GeLU - 48 192 12288
GPT-NeoX-20B Causal-Dec Next Token Parallel 50k BPE Layer Rotary GeLU ✓ 44 64 -
OPT (175B) Causal-Dec Next Token Standard - BPE - - ReLU ✓ 96 96 -
BLOOM (176B) Causal-Dec Next Token Standard 250k BPE Layer ALiBi GeLU ✓ 70 112 14336
Galactica (120B) Causal-Dec Next Token Standard 50k BPE+custom Layer Learned GeLU × 96 80 10240
GLaM (1.2T) MoE-Dec Next Token Standard 256k SentencePiece Layer Relative GeLU ✓ 64 128 32768
LaMDA (137B) Causal-Dec Next Token Standard 32k BPE Layer Relative GeGLU - 64 128 8192
MT-NLG (530B) Causal-Dec Next Token Standard 50k BPE Pre-Layer Learned GeLU ✓ 105 128 20480
AlphaCode (41B) Enc-Dec Next Token Multi-query 8k SentencePiece - - - - 64 128 6144
Chinchilla (70B) Causal-Dec Next Token Standard 32k SentencePiece-NFKC Pre-RMS Relative GeLU ✓ 80 64 8192
PaLM (540B) Causal-Dec Next Token Parallel+Multi-query 256k SentencePiece Layer RoPE SwiGLU × 118 48 18432
AlexaTM (20B) Enc-Dec Denoising Standard 150k SentencePiece Pre-Layer Learned GeLU ✓ 78 32 4096
Sparrow (70B) Causal-Dec Pref.&Rule RM - 32k SentencePiece-NFKC Pre-RMS Relative GeLU ✓ 16∗ 64 8192
U-PaLM (540B) Non-Causal-Dec MoD Parallel+Multi-query 256k SentencePiece Layer RoPE SwiGLU × 118 48 18432
UL2 (20B) Enc-Dec MoD Standard 32k SentencePiece - - - - 64 16 4096
GLM (130B) Non-Causal-Dec AR Blank Infilling Standard 130k SentencePiece Deep RoPE GeGLU ✓ 70 96 12288
CodeGen (16B) Causal-Dec Next Token Parallel - BPE Layer RoPE - - 34 24 -
LLaMA (65B) Causal-Dec Next Token Standard 32k BPE Pre-RMS RoPE SwiGLU - 80 64 8192
PanGu-Σ (1085B) Causal-Dec Next Token Standard - BPE Fused Layer - FastGeLU - 40 40 5120
BloombergGPT (50B) Causal-Dec Next Token Standard 131k Unigram Layer ALiBi GeLU ✓ 70 40 7680
Xuan Yuan 2.0 (176B) Causal-Dec Next Token Self 250k BPE Layer ALiBi GeLU ✓ 70 112 14336
CodeT5+ (16B) Enc-Dec SC+NT+Cont.+Match Standard - Code-Specific - - - - - - -
StarCoder (15.5B) Causal-Dec FIM Multi-query 49k BPE - Learned - - 40 48 6144
LLaMA-2 (70B) Causal-Dec Next Token Grouped-query 32k BPE Pre-RMS RoPE SwiGLUE - - - -
PaLM-2 - MoD Parallel - - - - - - - - -
LLaMA-3.1 (405B) Causal-Dec Next Token Grouped-query 128k BPE Pre-RMS RoPE SwiGLU - 126 128 16384
Nemotron-4 (340B) Causal-Dec Next Token Standard 256k SentencePiece - RoPE ReLU × 96 96 18432
DeepSeek (67B) Causal-Dec Next Token Grouped-query 100k BBPE Pre-RMS RoPE SwiGLU - 95 64 8192
DeepSeek-v2 (67B) MoE-Dec Next Token Multi-Head Latent 100k BBPE Pre-RMS RoPE SwiGLU - 60 128 5120

5.2. Evaluation Datasets and Tasks ous pre-trained LLMs in Table 10 and fine-tuned LLMs in Ta-
ble 11. We also compare the top-performing LLMs in various
The evaluation of LLMs is important in gauging their profi-
NLP tasks in Table 12.
ciency and limitations. This process measures the model’s abil-
ity to comprehend, generate, and interact with human language
across a spectrum of tasks. Evaluating a language model (LM) 5.2.1. Multi-task
is divided into two broader categories: 1) natural language un- MMLU [307]: A benchmark that measures the knowledge
derstanding (NLU) and 2) natural language generation (NLG). acquired by models during pretraining and evaluates models in
It is emphasized that tasks in NLU and NLG are softly catego- zero-shot and few-shot settings across 57 subjects, testing both
rized and are often used interchangeably in the literature. world knowledge and problem-solving ability.
Natural Language Understanding: It measures the language SuperGLUE [2]: A more challenging and diverse successor
understanding capacity of LMs. It encompasses multiple tasks, to the GLUE [309] benchmark, SuperGLUE includes a variety
including sentiment analysis, text classification, natural lan- of language understanding tasks, such as question answering,
guage inference (NLI), question answering (QA), common- natural language inference, and co-reference resolution. It is
sense reasoning (CR), mathematical reasoning (MR), reading designed to provide a rigorous test of language understanding
comprehension (RC), etc. and requires significant progress in areas like sample-efficient,
Natural Language Generation: It assesses the language gener- transfer, multi-task, and unsupervised or self-supervised learn-
ation capabilities of LLMs by understanding the provided input ing.
context. It includes tasks such as summarization, sentence com- BIG-bench [308]: The BIG-bench (Behavior of Intelligent
pletion, machine translation (MT), dialogue generation, etc. Generative Models Benchmark) is a large-scale benchmark de-
Numerous datasets are proposed for each task, evaluating signed to test the abilities of LLMs across a wide range of
LLMs against different characteristics. To provide an overview tasks, including reasoning, creativity, ethics, and understanding
of evaluation datasets, we briefly discuss a few famous datasets of specific domains.
within each category and offer a comprehensive list of datasets GLUE [309]: The General Language Understanding Evalua-
in Table 9. Moreover, we show a detailed overview of the train- tion (GLUE) benchmark is a collection of resources for train-
ing datasets and evaluation tasks and benchmarks used by vari- ing, evaluating, and analyzing natural language understanding
26
Table 6: Summary of optimization settings used for pre-trained LLMs. The values for weight decay, gradient clipping, and dropout are 0.1, 1.0, and 0.1, respectively,
for most of the LLMs.

Sequence LR Optimizers Precision Weight Grad

Models Batch Size Length LR Warmup Decay AdaFactorAdam AdamWFP16 BF16 Mixed Decay Clip Dropout
T5 (11B) 211 512 0.01 × inverse square root ✓ - - - - - ✓
GPT3 (175B) 32K - 6e-5 ✓ cosine ✓ ✓ ✓ ✓ -
mT5 (13B) 1024 1024 0.01 - inverse square root ✓ - - - - - ✓
PanGu-α (200B) - 1024 2e-5 - - - - - - ✓ - - - -
CPM-2 (198B) 1024 1024 0.001 - - ✓ - - - - - ✓
Codex (12B) - - 6e-5 ✓ cosine ✓ ✓ ✓ - -
ERNIE 3.0 (12B) 6144 512 1e-4 ✓ linear ✓ - - - ✓ - -
Jurassic-1 (178B) 3.2M 2048 6e-5 ✓ cosine ✓ ✓ ✓ ✓ -
HyperCLOVA (82B) 1024 - 6e-5 - cosine ✓ - - - ✓ - -
Yuan 1.0 (245B) <10M 2048 1.6e-4 ✓ cosine decay to 10% ✓ - - - ✓ - -
Gopher (280B) 3M 2048 4e-5 ✓ cosine decay to 10% ✓ ✓ - ✓ -
ERNIE 3.0 Titan (260B) - 512 1e-4 ✓ linear ✓ ✓ ✓ ✓ -
GPT-NeoX-20B 1538 2048 0.97e-5 ✓ cosine ✓ ✓ ✓ ✓ ×
OPT (175B) 2M 2048 1.2e-4 - linear ✓ ✓ ✓ ✓ ✓
BLOOM (176B) 2048 2048 6e-5 ✓ cosine ✓ ✓ ✓ ✓ ×
Galactica (120B) 2M 2048 7e-6 ✓ linear decay to 10% ✓ - - - ✓ ✓ ✓
GLaM (1.2T) 1M 1024 0.01 - inverse square root ✓ FP32 + ✓ - ✓ ×
LaMDA (137B) 256K - - - - - - - - - - - - -
MT-NLG (530B) 1920 2048 5e-5 ✓ cosine decay to 10% ✓ ✓ ✓ ✓ -
AlphaCode (41B) 2048 1536+768 1e-4 ✓ cosine decay to 10% ✓ ✓ ✓ ✓ -
Chinchilla (70B) 1.5M 2048 1e-4 ✓ cosine decay to 10% ✓ ✓ - - -
PaLM (540B) 2048 2048 0.01 - inverse square root ✓ - - - ✓ ✓ ×
AlexaTM (20B) 2M 1024 1e-4 - linear decay to 5% ✓ ✓ ✓ - ✓
U-PaLM (540B) 32 2048 1e-4 - cosine ✓ - - - - - -
UL2 (20B) 1024 1024 - - inverse square root - - - - - - × - -
GLM (130B) 4224 2048 8e-5 ✓ cosine ✓ ✓ ✓ ✓ ✓
CodeGen (16B) 2M 2048 5e-5 ✓ cosine ✓ - - - ✓ ✓ -
LLaMA (65B) 4M Tokens 2048 1.5e-4 ✓ cosine decay to 10% ✓ - - - ✓ ✓ -
PanGu-Σ (1.085T) 512 1024 2e-5 ✓ - ✓ ✓ - - -
BloombergGPT (50B) 2048 2048 6e-5 ✓ cosine ✓ ✓ ✓ ✓ ×
Xuan Yuan 2.0 (176B) 2048 2048 6e-5 ✓ cosine ✓ ✓ ✓ ✓ -
CodeT5+ (16B) 2048 1024 2e-4 - linear ✓ ✓ ✓ - -
StarCoder (15.5B) 512 8k 3e-4 ✓ cosine ✓ ✓ ✓ - -
LLaMA-2 (70B) 4M Tokens 4k 1.5e-4 ✓ cosine ✓ ✓ ✓ ✓ -
LLaMA-3.1 (405B) 16M 8192 8e-5 ✓ linear+cosine ✓ ✓ - - -
Nemotron-4 (340B) 2304 4096 - - linear - - - ✓ - - ×
DeepSeek (67B) 4608 4096 3.2e-4 ✓ cosine ✓ ✓ ✓ ✓ -
DeepSeek-v2 (67B) 9216 4k 2.4e-4 ✓ step-decay ✓ - - - ✓ ✓ -

Table 7: Summary of optimization settings used for instruction-tuned LLMs. Values for gradient clipping and dropout are the same as the pre-trained models, while
no model uses weight decay for instruction tuning.

Sequence Optimizers Grad

Models Batch Size Length LR Warmup LR_Decay AdaFactor Adam AdamW Clip Dropout
WebGPT (175B) BC:512, RM:32 - 6e-5 - - ✓ - -
T0 (11B) 1024 1280 1e-3 - - ✓ - ✓
Tk-Instruct (11B) 1024 - 1e-5 - constant - - - - -
OPT-IML (175B) 128 2048 5e-5 × linear ✓ ✓ ✓
Flan-U-PaLM (540B) 32 - 1e-3 - constant ✓ - ✓
Sparrow (70B) RM: 8+16, RL:16 - 2e-6 ✓ cosine decay to 10% ✓ ✓ ×
WizardCoder (15B) 512 2048 2e-5 ✓ cosine - - - - -
Alpaca (13B) 128 512 1e-5 ✓ cosine - - ✓ ✓ ×
Vicuna (13B) 128 -2048 2e-5 ✓ cosine ✓ - ×
LIMA (65B) 32 2048 1e-5 × linear ✓ - ✓

systems. It includes a variety of tasks that test a wide range of language text.
linguistic phenomena, making it a comprehensive tool for eval- CoQA [316]: A conversational question-answering dataset,
uating language understanding in AI. CoQA challenges models with questions that rely on conver-
sation history and require free-form text answers. Its diverse
5.2.2. Language Understanding content from seven domains makes it a rigorous test for mod-
WinoGrande [354]: A large-scale dataset inspired by the orig- els’ ability to handle a wide range of topics and conversational
inal Winograd [357] Schema Challenge tests models on their contexts.
ability to resolve pronoun ambiguity and encourages the devel- WiC [317]: This dataset assesses a model’s ability to dis-
opment of models that understand the broad context in natural cern word meanings based on context, aiding in tasks related
27
 Table 8: Details of various well-known pre-training and fine-tuning datasets. Here, alignment means aligning with human preferences.

Dataset Type Size/Samples Tasks Source Creation Comments

C4 [10] Pretrain 806GB - Common Crawl Automated A clean, multilingual dataset with billions
of tokens
mC4 [11] Pretrain 38.49TB - Common Crawl Automated A multilingual extension of the C4
dataset, mC4 identifies over 100 lan-
guages using cld3 from 71 monthly web
scrapes of Common Crawl.
Common Crawl, PubMed Central,
PILE [301] Pretrain 825GB - OpenWebText2, ArXiv, GitHub, Automated A massive dataset comprised of 22 con-
Books3, and others stituent sub-datasets
ROOTs [302] Pretrain 1.61TB - 498 Hugging Face datasets Automated 46 natural and 13 programming lan-
guages
MassiveWeb, Books, News,
MassiveText [116] Pretrain 10.5TB - Automated 99% of the data is in English
Wikipedia, Github, C4
Wikipedia [303] Pretrain - - Wikipedia Automated Dump of wikipedia
CommonCrawl, C4, Wikipedia,
RedPajama [304] Pretrain 5TB - Automated Open-source replica of LLaMA dataset
Github, Books, StackExchange
PushShift.io Reddit Pretrain 21.1GB - Reddit Automated Submissions and comments on Reddit
from 2005 to 2019
BigPython [140] Pretrain 5.5TB Coding GitHub Automated -
Pool of Prompt (P3) [17] Instructions 12M 62 PromptSource Manual A Subset of PromptSource, created from
177 datasets including summarization,
QA, classification, etc.
xP3 [154] Instructions 81M 71 P3+Multilingual datasets Manual Extending P3 to total 46 languages
Super-NaturalInstructions (SNI) [18] Instructions 12.4M 1616 Multiple datasets Manual Extending P3 with additional multi-
lingual datasets, total 46 languages
Flan [16] Instructions 15M 1836 Muffin+T0-SF+NIV2 Manual Total 60 languages
OPT-IML [97] Instructions 18.1M 1667 - Manual -
Self-Instruct [19] Instructions 82k 175 - Automated Generated 52k instructions with 82k sam-
ples from 175 seed tasks using GPT-3
Alpaca [158] Instructions 52k - - Automated Employed self-instruct method to gener-
ate data from text-davinci-003
Vicuna [159] Instructions 125k - ShareGPT Automated Conversations shared by users on
ShareGPT using public APIs
LLaMA-GPT-4 [160] Instructions 52k - Alpaca Automated Recreated Alpaca dataset with GPT-4 in
English and Chinese
Unnatural Instructions [305] Instructions 68k - 15-Seeds (SNI) Automated -
LIMA [185] Instructions 1k - Multiple datasets Manual Carefully created samples to test perfor-
mance with fine-tuning on less data
Anthropic-HH-RLHF [306] Alignment 142k - - Manual
Anthropic-HH-RLHF-2 [178] Alignment 39k - - Manual

to Word Sense Disambiguation. ability to understand and generate coherent and sensible stories.
Wikitext103 [318]: With over 100 million tokens from LAMBADA [335]: This dataset evaluates contextual text un-
Wikipedia’s top articles, this dataset is a rich resource for tasks derstanding through a word prediction task. Models must pre-
that require understanding long-term dependencies, such as lan- dict the last word of a passage, which is easy for humans when
guage modeling and translation. given the whole passage, but not when given only the last sen-
PG19 [319]: This is a digital library of diverse books from tence.
Project Gutenberg. It is specifically designed to facilitate re-
search in unsupervised learning and language modeling, with a
special focus on long-form content. 5.2.4. Physical Knowledge and World Understanding
C4 [10]: A clean, multilingual dataset, C4 offers billions of to- PIQA [340]: A dataset that probes the physical knowledge of
kens from web-crawled data. It is a comprehensive resource for models, aiming to understand how well they are learning about
training advanced Transformer models on various languages. the real world.
LCQMC [320]: The Large-scale Chinese Question Matching TriviaQA [341]: A dataset that tests models on reading com-
Corpus (LCQMC) is a dataset for evaluating the performance prehension and open domain question answering (QA) tasks,
of models in semantic matching tasks. It contains pairs of ques- with a focus on Information Retrieval (IR)-style QA.
tions in Chinese and their matching status, making it a valuable ARC [342]: A larger version of the ARC-Challenge, this
resource for research in Chinese language understanding. dataset contains both easy and challenging grade-school level,
multiple-choice science questions. It is a comprehensive test of
5.2.3. Story Cloze and Sentence Completion a model’s ability to understand and answer complex questions.
StoryCloze [334]: It introduces a new “StoryCloze Test”, a ARC-Easy [342]: A subset of the ARC dataset, ARC-
commonsense reasoning framework for evaluating story under- Easy, contains questions that are answered correctly by either
standing, generation, and script learning. It considers a model’s a retrieval-based algorithm or a word co-occurrence algorithm.
28
 Table 9: Categorized evaluation datasets used in evaluating LLMs.

Type Datasets/Benchmarks
Multi-Task MMLU [307], SuperGLUE [2], BIG-bench [308], GLUE [309], BBH [308], CUGE [310], Zero-
CLUE [311], FewCLUE [312], Blended Skill Talk [313], HELM [314], KLUE-STS [315]
Language Understanding CoQA [316], WiC [317], Wikitext103 [318], PG19 [319], LCQMC [320], QQP [321], WinoGender [322],
CB [323], FinRE [324], SanWen [325], AFQMC [311], BQ Corpus [326], CNSS [327], CKBQA 13 [328],
CLUENER [311], Weibo [329], AQuA [330], OntoNotes [331], HeadQA [332], Twitter Dataset [333]
Story Cloze and
StoryCloze [334], LAMBADA [335], LCSTS [336], AdGen [337], E2E [338], CHID [339], CHID-
Sentence Completion
FC [312]
Physical Knowledge and
PIQA [340], TriviaQA [341], ARC [342], ARC-Easy [342], ARC-Challenge [342], PROST [343], Open-
World Understanding
BookQA [344], WebNLG [345], DogWhistle Insider & Outsider [346]
Contextual Language RACE [347], RACE-Middle [347], RACE-High [347], QuAC [348], StrategyQA [349], Quiz Bowl [350],
Understanding cMedQA [351],cMedQA2 [352], MATINF-QA [353]
Commonsense Reasoning WinoGrande [354], HellaSwag [355], COPA [356], WSC [357], CSQA [358], SIQA [359], C3 [360],
CLUEWSC2020 [311], CLUEWSC [311], CLUEWSC-FC [312], ReCoRD [361]
Reading Comprehension SQuAD [362], BoolQ [363], SQUADv2 [364], DROP [365], RTE [366], WebQA [367], CMRC2017 [368],
CMRC2018 [369], CMRC2019 [370], COTE-BD [371], COTE-DP [371], COTE-MFW [371], Mul-
tiRC [372], Natural Questions [373], CNSE [327], DRCD [374], DuReader [375], Dureaderrobust [376],
DuReader-QG [375], SciQ [377], Sogou-log [378], Dureaderrobust -QG [376], QA4MRE [379], KorQuAD
1.0 [380], CAIL2018-Task1 & Task2 [381]
Mathematical Reasoning MATH [382], Math23k [383], GSM8K [384], MathQA [385], MGSM [386], MultiArith [387], AS-
Div [388], MAWPS [389], SVAMP [390]
Problem Solving HumanEval [141], DS-1000 [391], MBPP [392], APPS [382], CodeContests [142]
Natural Language Inference
ANLI [393], MNLI-m [394], MNLI-mm [394],QNLI [362], WNLI [357], OCNLI [311], CMNLI [311],
& Logical Reasoning
ANLI R1 [393], ANLI R2 [393], ANLI R3 [393], HANS [395], OCNLI-FC [312], LogiQA [396], Strate-
gyQA [349]
Cross-Lingual Understanding MLQA [397], XNLI [398], PAWS-X [399], XSum [400], XCOPA [401], XWinograd [402], TyDiQA-
GoldP [403], MLSum [404]
Truthfulness and Fact Checking TruthfulQA [405], MultiFC [406], Fact Checking on Fever [407]
Biases and Ethics in AI ETHOS [408], StereoSet [409], BBQ [410], Winobias [411], CrowS-Pairs [412]
Toxicity RealToxicityPrompts [413], CivilComments toxicity classification [414]
Language Translation WMT [415], WMT20 [416], WMT20-enzh [416], EPRSTMT [312], CCPM [417]
Scientific Knowledge AminoProbe [148], BioLAMA [148], Chemical Reactions [148], Galaxy Clusters [148], Mineral
Groups [148]
Dialogue Wizard of Wikipedia [418], Empathetic Dialogues [419], DPC-generated [96] dialogues, ConvAI2 [420],
KdConv [421]
Topic Classification TNEWS-FC [312], YNAT [315], KLUE-TC [315], CSL [311], CSL-FC [312], IFLYTEK [422]

It is a great starting point for models beginning to explore ad- tions. It is designed to evaluate the comprehension ability of
vanced question-answering. models in a more academic and challenging context.
ARC-Challenge [342]: A rigorous question-answering QuAC [348]: This dataset simulates an information-seeking
dataset, ARC-Challenge includes complex, grade-school level dialog between students and teachers using hidden Wikipedia
questions that demand reasoning beyond simple retrieval, test- text. It introduces unique challenges not found in machine com-
ing the true comprehension capabilities of models. prehension datasets, making it a valuable resource for advanc-
ing dialog systems.
5.2.5. Contextual Language Understanding
RACE [347]: The RACE dataset is a reading comprehension 5.2.6. Commonsense Reasoning
dataset collected from English examinations in China, which HellaSwag [355]: A dataset that challenges models to pick the
benchmarks AI models for understanding and answering ques- best ending to a context uses Adversarial Filtering to create a
tions on long and complex passages, simulating the challenge ‘Goldilocks’ zone of complexity, where generated text is absurd
of a real-world examination. to humans but often misclassified by models.
RACE-Middle [347]: Another subset of the RACE [347] COPA [401]: This dataset evaluates a model’s progress in
dataset, RACE-Middle, contains middle school-level English open-domain commonsense causal reasoning. Each question
exam questions. It offers a slightly less challenging but academ- comprises a premise and two alternatives, and the model must
ically oriented evaluation of a model’s comprehension skills. select the more plausible alternative, testing a model’s ability to
RACE-High [347]: A subset of the RACE [347] dataset, understand and reason about cause and effect.
RACE-High consists of high school-level English exam ques- WSC [357]: The Winograd Schema Challenge (WSC) is a
29
Table 10: An illustration of training datasets and evaluation tasks employed by pre-trained LLMs. Here, “QA” is question-answering, “Clf” is classification, “NLI”
is natural language inference, “MT” is machine translation, “RC” is reading comprehension, “CR” is commonsense reasoning, “MR” is mathematical reasoning,
“Mem.” is memorization.

Benchmark
Truthful/
BIG- Super Cloze/ Bias/
Models Training Dataset MMLU QA Clf NLI MT RC CR MR Coding
bench GLUE Completion Toxicity/
Mem.
T5 C4 [10] ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
GPT-3 Common Crawl, WebText, Books Cor- ✓ ✓ ✓ ✓ ✓ ✓
pora, Wikipedia
mT5 mC4 [11] ✓ ✓ ✓
PanGu-α 1.1TB Chinese Text Corpus ✓ ✓ ✓ ✓ ✓
CPM-2 WuDaoCorpus [109] ✓ ✓
Codex 54 million public repositories from Github ✓
ERNIE-3.0 Chinese text corpora, Baidu Search, Web ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
text, QA-long, QA-short, Poetry and Cou-
plet Domain-specific data from medical,
law, and financial area Baidu knowledge
graph with more than 50 million facts
Jurassic-1 Wikipedia, OWT, Books, C4, Pile [301], ✓ ✓ ✓ ✓
arXiv, GitHub
HyperCLOVA Korean blogs, Community sites, News, ✓
KiN Korean Wikipedia, Wikipedia (En-
glish and Japanese), Modu-Corpus: Mes-
senger, News, Spoken and written lan-
guage corpus, Web corpus
Yuan 1.0 Common Crawl, SogouT, Sogou News, ✓ ✓ ✓ ✓
Baidu Baike, Wikipedia, Books
Gopher subsets of MassiveWeb Books, C4, News, ✓ ✓ ✓ ✓ ✓ ✓ ✓
GitHub and Wikipedia samples from Mas-
siveText
ERNIE-3.0 TITAN Same as ERNIE 3.0 and ERNIE 3.0 ad- ✓ ✓ ✓ ✓ ✓
versarial dataset, ERNIE 3.0 controllable
dataset
GPT-NeoX-20B Pile [301] ✓ ✓ ✓ ✓ ✓ ✓
OPT RoBERTa [299], Pile [301], PushShift.io ✓ ✓ ✓ ✓
Reddit [423]
BLOOM ROOTs [13] ✓ ✓ ✓ ✓ ✓ ✓
Galactica arXiv, PMC, Semantic Scholar, Wikipedia, ✓ ✓ ✓ ✓ ✓
StackExchange, LibreText, Open Text-
books, RefSeq Genome, OEIS, LIPID
MAPS, NASAExoplanet, Common Crawl,
ScientificCC, AcademicCC, GitHub repos-
itories Khan Problems, GSM8K, OneS-
mallStep
GLaM Filtered Webpages, Social media conversa- ✓ ✓ ✓ ✓ ✓
tions Wikipedia, Forums, Books, News
LaMDA Infiniset : Public documents, Dialogs, Ut- ✓
terances
MT-NLG Two snapshots of Common Crawl and ✓ ✓ ✓ ✓ ✓
Books3, OpenWebText2, Stack Exchange,
PubMed Abstracts, Wikipedia, PG-19
[242], BookCorpus2, NIH ExPorter, Pile,
CC-Stories, RealNews
AlphaCode Selected GitHub repositories, CodeCon- ✓
tests: Codeforces, Description2Code, Co-
deNet
Chinchilla MassiveWeb, MassiveText Books, C4, ✓ ✓ ✓ ✓ ✓ ✓
News, GitHub, Wikipedia
PaLM webpages, books, Wikipedia, news, arti- ✓ ✓ ✓ ✓ ✓ ✓
cles, source code, social media conversa-
tions
AlexaTM Wikipedia, mC4 ✓ ✓ ✓ ✓ ✓
U-PaLM Same as PaLM ✓ ✓ ✓ ✓ ✓ ✓ ✓
UL2 - ✓ ✓ ✓ ✓ ✓ ✓
GLM-130B - ✓ ✓ ✓
CodeGen Pile, BigQuery, BigPython ✓
LLaMA CommonCrawl, C4, Github, Wikipedia, ✓ ✓ ✓ ✓ ✓ ✓ ✓
Books, arXiv, StackExchange
PanGu-Σ WuDaoCorpora, CLUE, Pile, C4, Python ✓ ✓ ✓ ✓ ✓ ✓
code
BloombergGPT inPile, Pile, C4, Wikipedia ✓ ✓ ✓ ✓ ✓ ✓ ✓
CodeT5+ CodeSearchNet, Github Code ✓ ✓
StarCoder The Stack v1.2 ✓ ✓ ✓ ✓
LLaMA-2 ✓ ✓ ✓ ✓ ✓ ✓ ✓
PaLM-2 Web documents, Code, Books, Maths, ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Conversation

30
 Table 11: An illustration of training datasets and evaluation benchmarks used in fine-tuned LLMs. “SNI” is a short of Super-NaturalInsturctions.

Truthful/
BIG-
Models Training Dataset MMLU BBH RAFT FLAN SNI PromptSource TyDiQA HumanEval MBPP Bias/
bench
Toxicity
T0 Pool of Prompts ✓
WebGPT ELI5 [424], ELI5 fact- ✓
check [166], TriviaQA [341],
ARC-Challenge [342], ARC-
Easy [342], Hand-written data,
Demonstrations of humans, Com-
parisons between model-generated
answers
Tk-INSTRUCT SNI [18] ✓
mT0 xP3 [154]
OPT-IML PromptSource [17], FLAN [16], ✓ ✓ ✓ ✓ ✓ ✓
SNI [425], UnifiedSKG [426],
CrossFit [427], ExMix [428],
T5 [10], Reasoning
Flan Muffin, T0-SF, NIv2, CoT ✓ ✓ ✓
WizardCoder Code Alpaca ✓ ✓

reading comprehension task in which a system must resolve of machine performance.

references in a text, often requiring world knowledge and rea-
soning about the text. 5.2.8. Mathematical Reasoning
CSQA [358]: The CommonsenseQA is a question-answering MATH [382]: This dataset is a platform for evaluating the
dataset that requires commonsense knowledge to evaluate the mathematical problem-solving abilities of AI models. It con-
ability of AI models to understand and answer questions. tains a diverse set of math problems, ranging from arithmetic
to calculus, and is designed to test the model’s ability to under-
stand and solve complex mathematical problems.
5.2.7. Reading Comprehension
Math23k [383]: This one challenges a model’s ability to un-
BoolQ [363]: A dataset derived from Google search queries, derstand and solve mathematical word problems. It contains
BoolQ challenges models to answer binary (yes/no) questions. 23,000 Chinese arithmetic word problems that require models
The questions are naturally occurring and are paired with a to perform reasoning and computation based on the problem
paragraph from a Wikipedia article containing the answer. It description.
is a test of reading comprehension and reasoning. GSM8K [384]: A dataset of diverse grade school math word
SQUADv2 [364]: The Stanford Question Answering Dataset problems, testing a model’s ability to perform multi-step math-
(SQuAD) [362] is a collection of questions posed by crowd ematical reasoning.
workers on a set of Wikipedia articles, where the answer to ev-
ery question is a segment of text from the corresponding reading 5.2.9. Problem Solving and Logical Reasoning
passage. SQuADv2 combines the original SQuAD1.1 dataset ANLI [393]: A large-scale dataset designed to test the robust-
with over 50,000 unanswerable questions. The aim is to evalu- ness of machine learning models in Natural Language Inference
ate a model’s ability to understand and answer questions based (NLI) is created through an iterative, adversarial process where
on a given context and to determine when a question is unan- humans try to generate examples that models cannot correctly
swerable. classify.
DROP [365]: DROP, or Discrete Reasoning Over the con- HumanEval [141]: A dataset for evaluating the problem-
tent of Paragraphs, is designed to test a model’s ability to un- solving ability of AI models, which includes a diverse set of
derstand a wide variety of reading phenomena. It encourages tasks that require various cognitive abilities, making it a com-
comprehensive and reliable evaluation of reading comprehen- prehensive tool for assessing general intelligence in AI.
sion capabilities. StrategyQA [349]: A question-answering dataset that re-
RTE [366]: The Recognizing Textual Entailment (RTE) quires reasoning over multiple pieces of evidence to evaluate
datasets come from a series of annual competitions on textual the strategic reasoning ability of AI models, pushing the bound-
entailment, predicting whether a given sentence logically fol- aries of what machines can understand and answer.
lows from another and evaluating a model’s understanding of
logical relationships in a text. 5.2.10. Cross-Lingual Understanding
WebQA [367]: A dataset for open-domain question answering, XNLI [398]: A cross-lingual benchmark, XNLI extends the
WebQA offers a large collection of web-based question-answer MultiNLI [429] corpus to 15 languages, including low-resource
pairs. It is designed to assess the ability of AI models to under- ones like Urdu. It tests models on cross-lingual sentence under-
stand and answer questions based on web content. standing, with 112,500 annotated pairs across three categories:
CMRC2018 [369]: This dataset is a test of Chinese language entailment, contradiction, and neutral.
models’ ability to reason comprehensively and is designed with PAWS-X [399]: PAWS-X, or Cross-lingual Paraphrase Adver-
a challenging span-extraction format that pushes the boundaries saries from Word Scrambling, is a multilingual version of the
31
PAWS [430] dataset for paraphrase identification. It includes similar to any AI system, are only as good as the data they have
examples in seven languages and is designed to evaluate the been trained on.
performance of cross-lingual paraphrase identification models. Medicine: The application of LLMs in the field of medicine is
reshaping healthcare delivery and research. For example, LLMs
5.2.11. Truthfulness are increasingly used in clinical decision support systems to
Truthful-QA [405]: A unique benchmark that measures a provide physicians with evidence-based treatment recommen-
language model’s truthfulness when generating answers. The dations [436, 437, 438]. By analyzing patient data and medical
dataset includes questions across various categories like health, literature, they can help identify potential diagnoses, suggest
law, and politics, some designed to test the model against com- appropriate tests, and recommend optimal treatment strategies.
mon human misconceptions. Moreover, LLMs can also enhance patient interactions with
healthcare systems; e.g., they can be used in chatbot applica-
5.2.12. Biases and Ethics in AI tions [439, 440, 441] to answer patient queries about symptoms
ETHOS [408]: ETHOS is a hate speech detection dataset or medications, schedule appointments, and even provide es-
built from YouTube and Reddit comments. It is a tool in the sential health advice. For medical research, LLMs are used to
fight against online hate speech, offering binary and multi-label extract and filter information from a considerable amount of
variants for robust content moderation. medical literature, identify relevant studies, summarize find-
StereoSet [409]: StereoSet is a comprehensive dataset de- ings, and even predict future research trends [442, 443, 444].
signed to measure and evaluate the presence of stereotypical For medical education, LLMs can help create training mate-
biases in language models. It focuses on four key domains: rials, generate exam questions, provide detailed explanations
gender, profession, race, and religion. Contrasting stereotypi- of complex medical topics, and offer personalized feedback to
cal bias against language modeling ability provides a valuable students [445, 446, 447, 448]. They can also simulate patient
tool for understanding and mitigating biases in large language interactions, enabling students to practice and improve their
models. clinical skills. At a broader level, LLMs can assist in public
health initiatives by analyzing media data to detect disease out-
6. Applications breaks, monitor public sentiment towards health policies, and
disseminate health information in a clear and understandable
Applying Large Language Models (LLMs) to a variety of manner [449]. LLMs can be employed to support public health
downstream tasks has become a popular trend in both AI- initiatives, addressing related issues such as data privacy, the
related research communities and industries, with many emerg- necessity for explainability, and the potential risk of propagat-
ing uses being discovered and explored daily. LLMs, which are ing biases [450, 451].
capable of understanding and generating human-like text, have Education: The integration of LLMs into the educational sec-
found meaningful applications across a variety of fields. This tor offers opportunities to enhance learning experiences, teacher
section provides an overview of LLM applications in medicine, support, and educational content development. For students, by
education, science, mathematics, law, finance, robotics, and analyzing their learning styles, performance, and preferences,
coding. While each of these domains pose different challenges, LLMs can provide customized study materials and practice
LLMs open up opportunities to make significant contributions questions to develop personalized learning experiences [452].
to these domains through their generalizability. For teachers, LLMs can help to create lesson plans and grade
General Purpose: LLMs are being widely considered as assignments and generate diverse and inclusive educational
general-purpose tools for a wide variety of tasks [431]. This content, significantly saving more time for teaching and student
is due to their inherent ability to understand, generate, and interaction [453, 454]. In language learning, LLMs serve as
manipulate human-like text in a contextually relevant man- advanced conversational partners capable of simulating conver-
ner. This allows them to perform tasks ranging from simple sations in multiple languages, correcting grammar, enhancing
language translation and question-answering to more complex vocabulary, and aiding pronunciation for the needs of fluency
tasks like summarization, text generation, and even program- in practice [455]. Furthermore, LLMs improve accessibility
ming help [432]. The utility of LLMs is further enhanced by in education by providing support for students with disabili-
their ability to adapt to the specific style and tone of the text ties. They can generate real-time transcriptions for the hear-
they are processing, making the outputs more user-friendly and ing impaired, offer reading assistance for the visually impaired,
context-aware. In everyday applications, LLMs can be used and simplify complex texts for those with learning disabili-
as personal assistants, helping users draft emails or schedule ties [451]. As LLMs continue to evolve, their applications in
appointments [433]; they can also be deployed in customer ser- education can benefit more students and teachers from different
vice to handle common questions or applied to generate content perspectives in practice.
for digital platforms like websites by creating human-like text Science: Similar to medical applications, LLMs can expedite
based on given prompts [434]. Moreover, LLMs play a cru- the research process by quickly analyzing and summarizing sci-
cial role in data analysis, where they can filter large volumes of entific literature. By briefing comprehensible and accessible re-
text data, summarize key points, and find patterns that would search summaries, LLMs can assist researchers in staying up-
take humans much longer to identify [435]. Despite their wide- to-date with the latest findings, even in fields outside their area
ranging applications, it is essential to remember that LLMs, of expertise [456, 457]. In addition, LLMs can aid scientists
32
Table 12: Performance comparison of top performing LLMs across various NLU and NLG tasks. Here, “N-Shots” indicate the number of example prompts provided
to the model during the evaluation, representing its capability in few-shot or zero-shot learning settings, “f” represents the fine-tuned version, and “B” represents the
benchmark.

Top-1 Top-2 Top-3

Task Dataset/Benchmark
Model (Size) Score (N-shots) Model (Size) Score (N-shots) Model (Size) Score (N-shots)
BIG-bench (B) Chinchilla (70B) 65.1 (5-shot) Gopher (280B) 53.97 (5-shot) PaLM (540B) 53.7 (5-shot)
Multi-Task
MMLU (B) GPT-4 (-) 86.4 (5-shot) Gemini (Ultra) 83.7 (5-shot) Flan-PaLM-2( f ) (Large) 81.2 (5-shot)
Language Understanding SuperGLUE (B) ERNIE 3.0 (12B) 90.6 (-) PaLM( f ) (540B) 90.4 (-) T5 (11B) 88.9 (-)
Story Comprehension and HellaSwag GPT-4 (-) 95.3 (10-shot) Gemini (Ultra) 87.8 (10-shot) PaLM-2 (Large) 86.8 (one shot)
Generation StoryCloze GPT3 (175B) 87.7 (few shot) PaLM-2 (Large) 87.4 (one shot) OPT (175B) 79.82 (-)
Physical Knowledge and PIQA PaLM-2 (Large) 85.0 (one shot) LLaMa (65B) 82.8 (zero shot) MT-NLG (530B) 81.99 (zero shot)
World Understanding TriviaQA PaLM-2 (Large) 86.1 (one shot) LLaMA-2 (70B) 85.0 (one shot) PaLM (540B) 81.4 (one shot)
Contextual Language
LAMBADA PaLM (540B) 89.7 (few shot) MT-NLG (530B) 87.15 (few shot) PaLM-2 (Large) 86.9 (one shot)
Understanding
WinoGrande GPT-4 (-) 87.5 (5-shot) PaLM-2 (Large) 83.0 (one shot) PaLM (540B) 81.1 (zero shot)
Commonsense Reasoning
SIQA LLaMA (65B) 52.3 (zero shot) Chinchilla (70B) 51.3 (zero shot) Gopher (280B) 50.6 (zero shot)
Reading Comprehension BoolQ PaLM( f ) (540B) 92.2 (-) T5 (11B) 91.2 (-) PaLM-2 (Large) 90.9 (one shot)
Truthfulness Truthful-QA LLaMA (65B) 57 (-)
MATH Gemini (Ultra) 53.2 (4-shot) PaLM-2 (Large) 34.3 (4-shot) LLaMa-2 (65B) 13.5 (4-shot)
Mathematical Reasoning
GSM8K GPT-4 (-) 92.0 (5-shot) PaLM-2 (Large) 80.7 (8-shot) U-PaLM (540B) 58.5 (-)
Problem Solving and
HumanEval Gemini( f ) (Ultra) 74.4 (zero shot) GPT-4 (-) 67.0 (zero shot) Code Llama (34B) 48.8 (zero shot)
Logical Reasoning

in formulating new hypotheses and research questions since to perform legal reasoning tasks [468] and answer legal ques-
their ability to process large-scale datasets allows them to un- tions [469].
veil insights that might not be immediately apparent to human Finance: LLMs like BloombergGPT [151], trained on exten-
researchers [458]. Moreover, for scientific writing, LLMs can sive proprietary financial datasets, exhibit superior performance
help researchers draft documents, suggest improvements, and on financial tasks. This indicates the value of domain-specific
ensure adherence to specific formatting guidelines [459, 460]. training in creating LLMs that can more accurately understand
This not only saves time but also improves the clarity of scien- and process industry-specific language and concepts. The intro-
tific communication, enabling interdisciplinary teams to work duction of FinGPT [470] as an open-source model offers trans-
together more effectively. parent and accessible resources to develop novel applications
Maths: In addition to providing mathematical research and such as robo-advising, algorithmic trading, and low-code so-
education support, LLMs can assist in solving mathematical lutions, ultimately expanding the capabilities of financial ser-
problems by giving step-by-step explanations and guiding users vices. Both BloombergGPT and FinGPT show the adaptabil-
through complex proofs and calculations. They can help iden- ity of LLMs to the financial domain, with the former showing
tify errors in reasoning or computation and suggest corrections, the power of custom datasets and the latter emphasizing a data-
serving as an invaluable tool for both learning and verification centric approach and low-rank adaptation techniques for cus-
purposes [461, 462]. LLMs can be employed to check the valid- tomization. Moreover, LLMs demonstrate an ability to break
ity of mathematical proofs, offering a preliminary filter before down complex financial tasks into actionable plans, enabling
human review. While they are not a substitute for the meticu- end-to-end solutions that were previously unfeasible with a sin-
lous work of mathematicians, they can help simplify the process gle model [471].
of proof verification [463, 464]. Moreover, LLMs enhance ac- Robotics: In robotics research, LLMs have promising appli-
cessibility to mathematics by translating complex concepts and cations, such as enhancing human-robot interaction [28, 472,
findings into understandable language for non-specialists [465], 473, 474], task planning [237], motion planning [246], nav-
where the gap between theoretical mathematics and applied igation [246, 475], object manipulation [236], personalized
contexts such as physics, engineering, and economics can be robots [476], etc. LLMs enable robots to understand the en-
bridged. vironment effectively and generate plans to complete tasks col-
Law: LLMs can assist with the thematic analysis of legal doc- laboratively [240, 26]. They can facilitate continuous learning
uments, including generating initial coding for datasets, iden- by allowing robots to access and integrate information from a
tifying themes, and classifying data according to these themes. wide range of sources, helping robots acquire new skills, adapt
This collaborative effort between legal experts and LLMs has to changes, and refine their paths [224, 233, 234].
proved to be effective in analyzing legal texts such as court
opinions on theft, improving both the efficiency and quality of
7. Challenges and Future Directions
the research [466]. Additionally, LLMs have been evaluated for
their ability to generate explanations of legal terms, focusing LLMs such as GPT-4 and its predecessors have significantly
on improving factual accuracy and relevance by incorporating advanced natural language processing. Nevertheless, they also
sentences from case law. By feeding relevant case law into the bring along a set of challenges. The computational cost, ad-
LLM, the augmented models can generate higher-quality expla- versarial robustness, and interpretability are among the tech-
nations with less factually incorrect information [467]. More- nical challenges that are intrinsic to these models. Further-
over, LLMs can be trained with specialized domain knowledge more, as these models are scaled up to handle more complex
33
tasks or to operate in more complex or dynamic environments, content that contradicts information they have generated
new challenges in scalability, privacy, and real-time processing earlier.
emerge. On the frontier of foundational research, integrating
• Fact-conflicting hallucination involves LLM’s generation
multi-modality and the effectiveness of transfer learning are be-
of content that does not align with established world
ing keenly explored. Additionally, the continuous learning as-
knowledge.
pect of these models, which aims to have models that can adapt
to new information over time, presents a fresh set of challenges. Prompt Engineering: Prompts serve as inputs to LLMs, and
These challenges not only underscore the technical intricacies their syntax and semantics play a crucial role in determining
involved but also highlight the broader impact and the future the model’s output. The prompt variations, sometimes counter-
trajectory of LLMs in real-world applications. The following intuitive to humans, can result in significant changes in model
sections delve into these challenges, shedding light on the on- output and are addressed through prompt engineering, which
going and potential efforts to address them. involves designing natural language queries to guide LLMs
Computational Cost: Training LLMs require extensive compu- responses effectively [484, 32].
tational resources, which increases production costs and raises Limited Knowledge: Information acquired during pretraining
environmental concerns due to substantial energy consump- is limited and may become obsolete after some time. Re-
tion during large-scale training. Improved performance occurs training the model using updated data is costly. To generate
as computational resources increase, but the rate of improve- factually accurate responses, people use a retrieval augmen-
ment gradually decreases when both the model and dataset tation pipeline [198]. However, pre-trained models are not
size remain fixed, following the power law of diminishing re- trained with retrieval augmentation generation (RAG) [6, 21];
turns [477]. hence, adapting the training pipeline is necessary [193, 25].
Bias and Fairness: LLMs can inherit and amplify societal bi- Safety and Controllability: Using LLMs comes with the risk
ases in their training data. These biases can manifest in the of generating harmful, misleading, or inappropriate content,
model’s outputs, leading to potential ethical and fairness is- whether by accident or when given specific prompts. Ensuring
sues [478]. these models are safely utilized is a significant concern [485].
Overfitting: Although LLMs possess substantial learning ca- Security and Privacy: LLMs are prone to leaking personal
pabilities, they are susceptible to overfitting noisy and peculiar information and generating false, unethical, misaligned re-
patterns within their extensive training data. Consequently, this sponses. Researchers have explored various security attacks,
may cause them to generate illogical responses [479]. The de- i.e., backdoor attacks, jailbreaking, prompt injection, and data
bate about Memorization vs. Generalization in LLMs is about poisoning, that lead to breaking LLMs security. Therefore,
finding the right balance. Memorization allows the model to developing better defense mechanisms is essential to ensure
remember specific details from its training data, ensuring it can LLMs are safe, reliable, and trustworthy for complex AI
provide accurate answers to precise questions. However, gen- applications [486].
eralization enables the model to make inferences and produce Multi-Modality: Multi-modal learning, where LLMs are
responses for inputs it has not seen before, which is essential trained on diverse data like text, images, and videos, aims to
for handling various real-world tasks. Striking the right bal- create models with richer understanding but faces challenges
ance is the challenge: too much memorization can lead to over- in data alignment, fusion strategies, and higher computational
fitting, making the model inflexible and struggling with new demands.
inputs [480]. Catastrophic Forgetting: LLMs are often pre-trained on
Economic and Research Inequality: The high cost of train- large datasets and then fine-tuned on domain-specific data,
ing and deploying LLMs may make their development concen- reducing training resources. However, they face issues like
trated within well-funded organizations, potentially worsening domain adaptation and catastrophic forgetting, which hinder
economic and research inequalities in AI [481]. the retention of original knowledge when learning new tasks.
Reasoning and Planning: Some reasoning and planning tasks, Adversarial Robustness: Large Language Models (LLMs)
even as seemingly simple as common-sense planning, which have shown great capabilities in various tasks but are vul-
humans find easy, remain well beyond the current capabilities nerable to adversarial attacks, where slight, deliberate input
of LLMs evaluated using an assessment framework. This is not alterations can mislead them. Especially with models like
entirely unexpected, considering that LLMs primarily generate BERT, adversarial fine-tuning can enhance robustness, al-
text completions based on likelihood and offer no solid guaran- though it sometimes compromises generalization [487]. As
tees in terms of reasoning abilities [482]. LLMs integrate more into complex systems, examining their
Hallucinations: LLMs exhibit “hallucinations", where they security properties becomes crucial, given the emerging field
generate responses that, while sounding plausible, are incorrect of adversarial attacks on LLMs within trustworthy ML [488].
or do not align with the provided information [483]. Hallucina- This vulnerability is notable in safety-critical domains, ne-
tions can be categorized into three categories. cessitating robust adversarial evaluation tools to ensure LLM
reliability [489].
• Input-conflicting hallucination, wherein LLMs produce
Interpretability and Explainability: The “black-box” nature
content that diverges from the input given by users.
of LLMs poses challenges in understanding their decision-
• Context-conflicting hallucination, where LLMs generate making, which is crucial for broader acceptance and trust,
34
especially in sensitive domains. Despite their advanced tively or negatively, emphasizing the need for proactive ethical
capabilities, the lack of insight into their operation limits their frameworks and policy measures to guide their responsible
effectiveness and trustworthiness [490, 491]. Efforts are being use and assign accountability for their outputs [497]. Auditing
made to make LLMs more explainable to promote user trust is identified as a promising governance mechanism to ensure
and to ensure responsible AI usage. Understanding the logic that AI systems, including LLMs, are designed and deployed
behind LLMs’ responses is essential for fostering trust and ethically, legally, and technically robust [498].
ensuring they align with human values and legal standards.
Privacy Concerns: Privacy concerns in Large Language
Models (LLMs) have escalated with their growth in complexity 8. Conclusion
and size, particularly around data sharing and potential misuse.
There is a risk of malicious content creation, filter bypass, This article has comprehensively reviewed the develop-
and data privacy issues, especially in e-commerce, where ments in LLMs. It contributes to summarizing significant
protecting customer privacy is crucial. If models are trained findings of LLMs in the existing literature and provides a
on private data, additional concerns arise if such models are detailed analysis of the design aspects, including architec-
made publicly available. LLMs tend to memorize phrases from tures, datasets, and training pipelines. We identified crucial
their training sets, which an adversary could exploit to extract architectural components and training strategies employed by
sensitive data, posing a threat to personal privacy [492, 493]. different LLMs. These aspects are presented as summaries
Real-Time Processing: Real-time processing in Large Lan- and discussions throughout the article. Moreover, we have
guage Models (LLMs) is pivotal for various applications, discussed the performance differences of LLMs in zero-shot
especially with the rising popularity of mobile AI applications and few-shot settings, explored the impact of fine-tuning, and
and concerns regarding information security and privacy. compared supervised and generalized models and encoder vs.
However, LLMs often have hundreds of layers and millions decoder vs. encoder-decoder architectures. A comprehensive
of parameters, which impede real-time processing due to the review of multi-modal LLMs, retrieval augmented LLMs,
high computational demands and limited weight storage on LLMs-powered agents, efficient LLMs, datasets, evaluation,
hardware platforms, particularly in edge computing environ- applications, and challenges is also provided. This article is
ments [494]. While certain efforts like MobileBERT aim anticipated to serve as a valuable resource for researchers,
to reduce memory requirements, they still face substantial offering insights into the recent advancements in LLMs and
execution overhead due to the large number of model layers, providing fundamental concepts and details to develop better
leading to high inference latency. LLMs.
Long-Term Dependencies: Large Language Models have
shown considerable progress in understanding and generating Acknowledgement: The author/s would like to acknowl-
text, yet they often struggle with preserving context and edge the support received from Saudi Data and AI Authority
handling long-term dependencies, particularly in complex, (SDAIA) and King Fahd University of Petroleum and Miner-
multi-turn conversations or long documents. This limitation als (KFUPM) under SDAIA-KFUPM Joint Research Center for
can lead to incoherent or irrelevant responses. Artificial Intelligence Grant No. JRC-AI-RFP-11.
Hardware Acceleration: The growth of LLMs presents signif-
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