Tutorial 10b - LLM

The document outlines a tutorial on the application of Large Language Models (LLMs) in Knowledge Representation and Reasoning (KRR). It includes questions on LLM functionalities, model comparisons, and techniques for fine-tuning LLMs for specific tasks, such as legal document analysis and summarization. Additionally, it discusses concepts like Retrieval-Augmented Generation and the differences between Full Fine-Tuning and Parameter-Efficient Fine-Tuning.

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Tutorial 10b - LLM

Uploaded by

Simon Lau Boung Yew

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COMP2024 Spring 2025

Tutorial 10 – LLM for Knowledge and Reasoning

1. How do Large Language Models (LLMs) contribute to Knowledge Representation

and Reasoning (KRR)?

2. What are probability-based language models, and how do they function?

3. Explain the key components of a Transformer model.

4. Compare BERT and GPT in terms of knowledge processing.

5. What is Retrieval-Augmented Generation (RAG), and why is it useful?

6. Compare Full Fine-Tuning and Parameter-Efficient Fine-Tuning (PEFT).

7. (Question for further study): A research lab wants an LLM-powered assistant to

analyse legal case documents and infer potential outcomes. Suggest how you
may implement a reasoning-based application using LLMs.

8. (Question for further study): Suggest a technique you will apply (with the steps) to
fine-tune and optimize an LLM for a domain-specific task.
Problem:
A company wants to fine-tune an LLM for legal document summarization.

Prepared by Simon Lau Boung Yew Page 1 of 1

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Tutorial 10b - LLM

Uploaded by

Tutorial 10b - LLM

Uploaded by

COMP2024 Spring 2025

Tutorial 10 – LLM for Knowledge and Reasoning

1. How do Large Language Models (LLMs) contribute to Knowledge Representation

2. What are probability-based language models, and how do they function?

3. Explain the key components of a Transformer model.

4. Compare BERT and GPT in terms of knowledge processing.

5. What is Retrieval-Augmented Generation (RAG), and why is it useful?

6. Compare Full Fine-Tuning and Parameter-Efficient Fine-Tuning (PEFT).

7. (Question for further study): A research lab wants an LLM-powered assistant to

Prepared by Simon Lau Boung Yew Page 1 of 1

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