awesome-papers-for-rag

A curated list of resources dedicated to retrieval-augmented generation (RAG).

The retrieval-augmented generation (RAG) is to combine the merits of retrieval system and llm to generation high-quality answers for users.

The Framework for RAG System

Typically, the rag system consists of a set of modules, where each task are described as follows:

Components	Input	Output	Tasks
Intent Clarify	question	search queries	Query performance prediction, Query (intent) classification, Query expasion, et al.
Retrieval	question/queries	documents/passages	Ad-hoc retrieval, Document retrieval, Passage retrieval, et al.
Mediation	questions+documents	contexts	Re-ranking, Context compression, post-retrieval, et al.
Generation	question+contexts	answer	Question answering, summarization, et al.
Result Enhancement	question+answer+contexts	answer	Claim verification, Attribution, et al.

Healthcheck

pip3 install -r requirements.txt
python3 healthcheck.py

Surveys for RAG

• The Organization column only record the organization of the first author.

Date	Title	Organization	Code
2024/09/16	Trustworthiness in Retrieval-Augmented Generation Systems: A Survey	Tsinghua University	Code</br>
2024/09/10	Graph Retrieval-Augmented Generation: A Survey	Peking University	Code</br>
2024/02/29	Retrieval-Augmented Generation for AI-Generated Content: A Survey	Peking University	Code</br>
2024/01/03	Retrieval-Augmented Generation for Large Language Models: A Survey	Tongji University	Code</br>
2024/01/03	A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models	Islamic University of Technology	No
2023/12/07	Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications	Harbin Institute of Technology	No
2023/09/19	The Rise and Potential of Large Language Model Based Agents: A Survey	Fudan NLP Group	Code</br>
2023/08/14	Large Language Models for Information Retrieval: A Survey	Renmin University	Code</br>
2022/02/02	A Survey on Retrieval-Augmented Text Generation	Nara Institute of Science and Techonology	No

Systems for RAG

• The Organization column only record the organization of the first author.

Date	Title	Organization	Code
2024/11/07	LightRAG: Simple and Fast Retrieval-Augmented Generation	BUPT	Code
2024/10/25	StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization	ISCAS	Code
2024/08/21	RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation	Nanjing University	Code
2024/07/11	Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting	University of California, San Diego	No
2024/06/19	InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales	University of Virginia	Code
2024/05/22	FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research	Renmin University of China	Code
2024/04/24	From Local to Global: A Graph RAG Approach to Query-Focused Summarization	Microsoft	Code
2023/11/22	FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation	Google	Code
2023/11/08	PDFTriage: Question Answering over Long, Structured Documents	Stanford	Code
2023/10/27	WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia	Stanford	Code
2023/10/27	LeanDojo: Theorem Proving with Retrieval-Augmented Language Models	Caltech	Code
2023/06/13	WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences	Tsinghua University	Code
2023/05/23	WebCPM: Interactive Web Search for Chinese Long-form Question Answering	Tsinghua University	Code
2022/06/01	WebGPT: Browser-assisted question-answering with human feedback	Open AI	No

Evaluations for RAG

• The Organization column only record the organization of the first author.

Date	Title	Organization	Code
2024/10/10	HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly	Princeton	Code
2024/08/16	RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models	NewsBreak	Code </br>
2024/08/16	RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation	Amazon	Code </br>
2024/05/13	Evaluation of Retrieval-Augmented Generation: A Survey	Tencent	Code
2024/04/21	Evaluating Retrieval Quality in Retrieval-Augmented Generation	UMASS	No
2024/04/08	FaaF: Facts as a Function for the evaluation of generated text	IMMO Capital	Code
2024/02/19	CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models	University of Science and Technology of China	Code
2024/1/30	RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture	Microsoft	No
2024/1/11	Seven Failure Points When Engineering a Retrieval Augmented Generation System	Applied Artificial Intelligence Institute	No
2023/12/20	Benchmarking Large Language Models in Retrieval-Augmented Generation	Chinese Information Processing Laboratory	Code
2023/11/16	ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems	Stanford	Code
2023/11/14	RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge	Peking University	No
2023/10/31	Enabling Large Language Models to Generate Text with Citations	Princeton University	Code
2023/09/26	RAGAS: Automated Evaluation of Retrieval Augmented Generation	Exploding Gradients	Code
2021/08/05	TruLens:Evaluation and Tracking for LLM Experiments	TruEra	Code