This repository is for our ISSTA 2024 paper: A Large-Scale Empirical Study on Improving the Fairness of Image Classification Models

Official implementation of Würstchen: Efficient Pretraining of Text-to-Image Models

This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.

Code for ACL 2024 paper "TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space"

official implementation of "Interpreting CLIP's Image Representation via Text-Based Decomposition"

[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"

[WSDM'2024 Oral] "DiffKG: Knowledge Graph Diffusion Model for Recommendation"

[CIKM 2023] This is the code repo for our CIKM‘23 paper "Text Matching Improves Sequential Recommendation by Reducing Popularity Biases".

Open source implementation of "Vision Transformers Need Registers"

Code of Paper "ReLLa: Retrieval-enhanced Large Language Models for Mitigating Long Context Problems in Recommendation".

PyTorch impelementation for "Federated Recommendation via Hybrid Retrieval Augmented Generation".

Harnessing 1.4M GPT4V-synthesized Data for A Lite Vision-Language Model

[EMNLP 2023] Enabling Large Language Models to Generate Text with Citations. Paper: https://arxiv.org/abs/2305.14627

We jailbreak GPT-3.5 Turbo’s safety guardrails by fine-tuning it on only 10 adversarially designed examples, at a cost of less than $0.20 via OpenAI’s APIs.

Code and documents of LongLoRA and LongAlpaca (ICLR 2024 Oral)

All code necessary for reproducing the "Improving Sequential Recommendation with LLMs" and "Leveraging Large Language Models for Sequential Recommendation" papers.

[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"

[EMNLP 2023] Code and Data for "SOUL: Towards Sentiment and Opinion Understanding of Language"

Pytorch implementation of DoReMi, a method for optimizing the data mixture weights in language modeling datasets

The implementation for the work "CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation".