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https://github.com/jxzhangjhu/Awesome-LLM-RAG
Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models
https://github.com/jxzhangjhu/Awesome-LLM-RAG
List: Awesome-LLM-RAG
embeddings large-language-models llm rag rag-embeddings retrieval-augmented-generation retrieval-information
Last synced: about 1 month ago
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Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models
- Host: GitHub
- URL: https://github.com/jxzhangjhu/Awesome-LLM-RAG
- Owner: jxzhangjhu
- Created: 2023-10-26T17:47:05.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-08T22:53:18.000Z (7 months ago)
- Last Synced: 2024-05-22T06:00:48.769Z (7 months ago)
- Topics: embeddings, large-language-models, llm, rag, rag-embeddings, retrieval-augmented-generation, retrieval-information
- Homepage:
- Size: 34.2 KB
- Stars: 602
- Watchers: 5
- Forks: 33
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Awesome-LLM-RAG
\
[](https://github.com/hee9joon/Awesome-Diffusion-Models)
[](https://opensource.org/licenses/MIT)
[](https://github.com/chetanraj/awesome-github-badges)
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](#resources)
- [Workshops and Tutorials](#workshops-and-tutorials)
- [Papers](#papers)
- [Survey and Benchmark](#survey-and-benchmark)
- [Retrieval-enhanced LLMs](#retrieval-enhanced-llms)
- [RAG Instruction Tuning](#rag-instruction-tuning)
- [RAG In-Context Learning](#rag-in-context-learning)
- [RAG Embeddings](#rag-embeddings)
- [RAG Simulators](#rag-simulators)
- [RAG Search](#rag-search)
- [RAG Long-text and Memory](#rag-long-text-and-memory)
- [RAG Evaluation](#rag-evaluation)
- [RAG Optimization](#rag-optimization)
- [RAG Application](#rag-application)
---
# 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](https://sites.google.com/view/pgai2023/home)]
**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](https://rgm-cikm23.github.io/)]
**First Workshop on Generative Information Retrieval**
*Gabriel Bénédict, Ruqing Zhang, Donald Metzler*
SIGIR 23 – Jul 2023 [[link](https://coda.io/@sigir/gen-ir)]
**Retrieval-based Language Models and Applications**
*Akari Asai, Sewon Min, Zexuan Zhong, Danqi Chen*
ACL 23 – Jul 2023 [[link](https://acl2023-retrieval-lm.github.io/)]
**Become a Generative AI Developer**
*Richie Cotton, Olivier Mertens, Korey Stegared-Pace, James Briggs, Vincent Vankrunkelsven, Alara Dirik, Jacob Marquez, Priyanka Asnani*
DataCamp [[link](https://www.datacamp.com/ai-code-alongs)]
# Papers
## Survey and Benchmark
**Benchmarking Large Language Models in Retrieval-Augmented Generation** \
*Jiawei Chen, Hongyu Lin, Xianpei Han, Le Sun* \
arXiv 2023. [[Paper](https://arxiv.org/abs/2309.01431)][[Github](https://github.com/chen700564/RGB)] \
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](https://arxiv.org/abs/2311.09210)]
**REST: Retrieval-Based Speculative Decoding** \
*Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D Lee, Di He* \
arXiv - Nov 2023 [[Paper](https://arxiv.org/abs/2311.08252)][[Github](https://github.com/fasterdecoding/rest)]
**Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection**
*Anonymous*
ICLR 24 – Oct 2023 [[paper](https://openreview.net/forum?id=hSyW5go0v8)]
**Self-Knowledge Guided Retrieval Augmentation for Large Language Models** \
*Yile Wang, Peng Li, Maosong Sun, Yang Liu* \
arXiv - Oct 2023 [[Ppaer](https://arxiv.org/abs/2310.05002)]
**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](https://arxiv.org/abs/2310.03025)]
**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](https://arxiv.org/abs/2310.03714)] [[code](https://github.com/stanfordnlp/dspy)]
**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](https://arxiv.org/abs/2305.13300)] [[code](https://github.com/OSU-NLP-Group/LLM-Knowledge-Conflict)]
**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](https://arxiv.org/abs/2305.06983)] [[code](https://github.com/jzbjyb/FLARE)]
**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](https://arxiv.org/abs/2301.12652)]
**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](https://arxiv.org/abs/2005.11401)]
## RAG Instruction Tuning
**RA-DIT: Retrieval-Augmented Dual Instruction Tuning**
*Anonymous*
ICLR 24 – Oct 23 [[paper](https://openreview.net/forum?id=22OTbutug9)]
**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](https://openreview.net/forum?id=4stB7DFLp6)]
## 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](https://uploads-ssl.webflow.com/60fd4503684b466578c0d307/63c6c20dec4479564db21819_NEW_In_Context_Retrieval_Augmented_Language_Models.pdf)] [[code](https://github.com/AI21Labs/in-context-ralm)]
## 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](https://arxiv.org/abs/2310.10567)][[Github](https://github.com/TrustedLLM/RegaVAE)]
**Text Embeddings Reveal (Almost) As Much As Text** \
*John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush* \
EMNLP 2023 - Oct 2023 [[Paper](https://arxiv.org/abs/2310.06816?ref=upstract.com)][[Github](https://github.com/jxmorris12/vec2text)]
**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](https://arxiv.org/abs/2310.19923)][[Model](https://huggingface.co/jinaai/jina-embeddings-v2-small-en)]
## RAG Simulators
**KAUCUS: Knowledge Augmented User Simulators for Training Language Model Assistants** \
*Kaustubh D. Dhole* \
Simulation of Conversational Intelligence in Chat, EACL 2024 [[Paper](https://arxiv.org/abs/2401.16454)]
## 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](https://arxiv.org/abs/2405.14831)] [[GitHub](https://github.com/OSU-NLP-Group/HippoRAG)]
**Understanding Retrieval Augmentation for Long-Form Question Answering** \
*Hung-Ting Chen, Fangyuan Xu, Shane A. Arora, Eunsol Choi* \
arXiv - Oct 2023 [[Paper](https://arxiv.org/abs/2310.12150)]
## 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](https://arxiv.org/abs/2311.09476)] [[Github](https://github.com/stanford-futuredata/ares)]
## 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](https://arxiv.org/abs/2311.08377)][[Github](https://github.com/zorazrw/filco)]
**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](https://arxiv.org/abs/2302.00093)][[Github](https://github.com/google-research-datasets/GSM-IC)]
**Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks** \
*Akari Asai, Matt Gardner, Hannaneh Hajishirzi* \
NAACL 2022 - Dec 2021 [[Paper](https://arxiv.org/abs/2112.08688)][[Github](https://github.com/akariasai/evidentiality_qa)]
**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](https://arxiv.org/abs/2212.10511)][[Github](https://github.com/alextmallen/adaptive-retrieval)]
## RAG Application
**Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination** \
*Haoqiang Kang, Xiao-Yang Liu* \
arXiv - Nov 2023 [[Paper](https://arxiv.org/abs/2311.15548)]
**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](https://arxiv.org/abs/2310.16146v1)]
**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](https://arxiv.org/abs/2311.09180)]