Awesome-LLM-RAG

Awesome-LLM-RAG

Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models

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This repository, Awesome-LLM-RAG, aims to record advanced papers on Retrieval Augmented Generation (RAG) in Large Language Models (LLMs). It serves as a resource hub for researchers interested in promoting their work related to LLM RAG by updating paper information through pull requests. The repository covers various topics such as workshops, tutorials, papers, surveys, benchmarks, retrieval-enhanced LLMs, RAG instruction tuning, RAG in-context learning, RAG embeddings, RAG simulators, RAG search, RAG long-text and memory, RAG evaluation, RAG optimization, and RAG applications.

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Awesome-LLM-RAG


Awesome License: MIT Made With Love

This repo aims to record advanced papers of Retrieval Agumented Generation (RAG) in LLMs.

We strongly encourage the researchers that want to promote their fantastic work to the LLM RAG to make pull request to update their paper's information!


Contents


Resources

Workshops and Tutorials

Personalized Generative AI
Zheng Chen, Ziyan Jiang, Fan Yang, Zhankui He, Yupeng Hou, Eunah Cho, Julian McAuley, Aram Galstyan, Xiaohua Hu, Jie Yang
CIKM 23 – Oct 2023 [link]

First Workshop on Recommendation with Generative Models
Wenjie Wang, Yong Liu, Yang Zhang, Weiwen Liu, Fuli Feng, Xiangnan He, Aixin Sun
CIKM 23 – Oct 2023 [link]

First Workshop on Generative Information Retrieval
Gabriel Bénédict, Ruqing Zhang, Donald Metzler
SIGIR 23 – Jul 2023 [link]

Retrieval-based Language Models and Applications
Akari Asai, Sewon Min, Zexuan Zhong, Danqi Chen
ACL 23 – Jul 2023 [link]

Become a Generative AI Developer Richie Cotton, Olivier Mertens, Korey Stegared-Pace, James Briggs, Vincent Vankrunkelsven, Alara Dirik, Jacob Marquez, Priyanka Asnani DataCamp [link]

Papers

Survey and Benchmark

Benchmarking Large Language Models in Retrieval-Augmented Generation
Jiawei Chen, Hongyu Lin, Xianpei Han, Le Sun
arXiv 2023. [Paper][Github]
4 Sep 2023

Retrieval-enhanced LLMs

Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, Dong Yu
arxiv - Nov 2023 [Paper]

REST: Retrieval-Based Speculative Decoding
Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D Lee, Di He
arXiv - Nov 2023 [Paper][Github]

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Anonymous
ICLR 24 – Oct 2023 [paper]

Self-Knowledge Guided Retrieval Augmentation for Large Language Models
Yile Wang, Peng Li, Maosong Sun, Yang Liu
arXiv - Oct 2023 [Ppaer]

Retrieval meets Long Context Large Language Models
Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, Bryan Catanzaro
arxiv - Oct 2023 [Paper]

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, Christopher Potts
arXiv – Oct 2023 [paper] [code]

Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts
Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, Yu Su
ICLR 24 – May 2023 [paper] [code]

Active Retrieval Augmented Generation
Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig
arXiv – May 2023 [paper] [code]

REPLUG: Retrieval-Augmented Black-Box Language Models
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, Wen-tau Yih
arXiv – Jan 2023 [paper]

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela NeurIPS 2020 - May 2020 [Paper]

RAG Instruction Tuning

RA-DIT: Retrieval-Augmented Dual Instruction Tuning
Anonymous
ICLR 24 – Oct 23 [paper]

InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining
Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, Bryan Catanzaro
arXiv - Oct 23 [paper]

RAG In-Context Learning

In-Context Retrieval-Augmented Language Models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham
AI21 Labs – Jan 2023 [paper] [code]

RAG Embeddings

RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling
Jingcheng Deng, Liang Pang, Huawei Shen, Xueqi Cheng
EMNLP 2023 - Oct 2023 [Paper][Github]

Text Embeddings Reveal (Almost) As Much As Text
John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush
EMNLP 2023 - Oct 2023 [Paper][Github]

Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents
Michael Günther, Jackmin Ong, Isabelle Mohr, Alaeddine Abdessalem, Tanguy Abel, Mohammad Kalim Akram, Susana Guzman, Georgios Mastrapas, Saba Sturua, Bo Wang, Maximilian Werk, Nan Wang, Han Xiao
arXiv - Oct 2023. [Paper][Model]

RAG Simulators

KAUCUS: Knowledge Augmented User Simulators for Training Language Model Assistants
Kaustubh D. Dhole
Simulation of Conversational Intelligence in Chat, EACL 2024 [Paper]

RAG Search

RAG Long-text and Memory

HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, Yu Su
arXiv - May 2024 [paper] [GitHub]

Understanding Retrieval Augmentation for Long-Form Question Answering
Hung-Ting Chen, Fangyuan Xu, Shane A. Arora, Eunsol Choi
arXiv - Oct 2023 [Paper]

RAG Evaluation

ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
Jon Saad-Falcon, Omar Khattab, Christopher Potts, Matei Zaharia
arXiv - Nov 2023. [Paper] [Github]

RAG Optimization

Learning to Filter Context for Retrieval-Augmented Generation
Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, Graham Neubig
arxiv- Nov 2023 [Paper][Github]

Large Language Models Can Be Easily Distracted by Irrelevant Context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, Denny Zhou
ICML 2023 - Jan 2023 [Paper][Github]

Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks
Akari Asai, Matt Gardner, Hannaneh Hajishirzi
NAACL 2022 - Dec 2021 [Paper][Github]

When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, Hannaneh Hajishirzi
ACL 2023 - Dec 2022 [Paper][Github]

RAG Application

Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination
Haoqiang Kang, Xiao-Yang Liu
arXiv - Nov 2023 [Paper]

Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature
Alejandro Lozano, Scott L Fleming, Chia-Chun Chiang, Nigam Shah
arXiv - Oct 2023. [Paper]

PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers
Sheshera Mysore, Zhuoran Lu, Mengting Wan, Longqi Yang, Steve Menezes, Tina Baghaee, Emmanuel Barajas Gonzalez, Jennifer Neville, Tara Safavi
arXiv - Nov 2023. [Paper]

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