RAG-Reading-List

This repository is used to collect recent studies on RAG methods, benchmarks, and toolkits. Welcome any updates through pull requests.

📄 Method

Text only

1. Measuring and Narrowing the Compositionality Gap in Language Models
   • EMNLP 2023 Findings, 2022-10-07, https://aclanthology.org/2023.findings-emnlp.378/
   • Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah Smith, Mike Lewis
   • This paper proposes self-ask, an elicitive prompting strategy that ask the LLM itself to generate the decomposition of a complex query. The Bamboogle dataset is also created by this paper.
2. Answering Questions by Meta-Reasoning over Multiple Chains of Thought
   • EMNLP 2023, 2023-04-25, https://aclanthology.org/2023.emnlp-main.364/
   • Ori Yoran, Tomer Wolfson, Ben Bogin, Uri Katz, Daniel Deutch, Jonathan Berant
   • This paper proposes a multi-chain reasoning method (meta-CoT) that combining multiple reasoning chains to infer the final answer.
3. In-Context Retrieval-Augmented Language Models
   • arXiv, 2023-08-01, https://arxiv.org/abs/2302.00083
   • Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham
   • Using retrieved results can effectively imrpove LLMs' performance. Retrieve after every $k$ tokens, and the performance can be improved when $k$ is small (more frequent retrieval). Reranking is also helpful.
4. PlanXRAG: Planning-guided Retrieval Augmented Generation
   • arXiv, 2024-10-28, https://arxiv.org/abs/2410.20753
   • Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha, Arno Solin, Nagarajan Natarajan, Amit Sharma
5. RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation
   • arXiv, 2024-03-31 , https://arxiv.org/abs/2404.00610
   • Chi-Min Chan, Chunpu Xu, Ruibin Yuan, Hongyin Luo, Wei Xue, Yike Guo, Jie Fu
6. Retrieval-Augmented Generation with Estimation of Source Reliability
   • arXiv, 2024-10-30, https://arxiv.org/abs/2410.22954
   • Jeongyeon Hwang, Junyoung Park, Hyejin Park, Sangdon Park, Jungseul Ok
7. RuleRAG: Rule-guided retrieval-augmented generation with language models for question answering
   • arXiv, 2024-10-15, https://arxiv.org/abs/2410.22353v1
   • Zhongwu Chen, Chengjin Xu, Dingmin Wang, Zhen Huang, Yong Dou, Jian Guo
8. SmartRAG: Jointly Learn RAG-Related Tasks From the Environment Feedback
   • arXiv, 2024-10-22, https://arxiv.org/abs/2410.18141v1
   • Jingsheng Gao, Linxu Li, Weiyuan Li, Yuzhuo Fu, Bin Dai
9. RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation
   • arXiv, 2024-11-6, https://arxiv.org/abs/2411.03920
   • Ian Poey, Jiajun Liu, Qishuai Zhong, Adrien Chenailler
10. Agentic Information Retrieval
    • arXiv, 2024-10-29, https://arxiv.org/abs/2410.09713
    • Weinan Zhang, Junwei Liao, Ning Li, Kounianhua Du
11. TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation
    • EMNLP 2024 (Findings), 2024-6-17, https://aclanthology.org/2024.findings-emnlp.496
    • Jinyuan Fang, Zaiqiao Meng, Craig MacDonald
    • This paper proposes method TRACE that extracts logically connected knowledge triples from the retrieved docuemnts.

Multimodal

1. Murag: Multimodal retrieval-augmented generator for open question answering over images and text.
   • EMNLP, 2022-12-7 https://aclanthology.org/2022.emnlp-main.375.pdf
   • Wenhu Chen, Hexiang Hu, Xi Chen, Pat Verga, William W. Cohen
2. MLLM IS A STRONG RERANKER: ADVANCING MULTIMODAL RETRIEVAL-AUGMENTED GENERATION VIA KNOWLEDGE-ENHANCED RERANKING AND NOISEINJECTED TRAINING
   • arXiv, 2024-9-25, https://arxiv.org/pdf/2407.21439
   • Zhanpeng Chen, Chengjin Xu, Yiyan Qi, Jian Guo
3. Retrieval-Augmented Multimodal Language Modeling
   • ICML, 2023-7-23, https://dl.acm.org/doi/10.5555/3618408.3620067
   • Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Rich James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, Wen-tau Yih
4. Enhancing Multi-modal Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generation
   • MM, 2023-10-29, https://dl.acm.org/doi/pdf/10.1145/3581783.3611964
   • Qian Yang, Qian Chen, Wen Wang, Baotian Hu, Min Zhang
5. An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language Models
   • VLDB, 2024-11-8, https://dl.acm.org/doi/10.14778/3685800.3685868
   • Mengzhao Wang, Haotian Wu, Xiangyu Ke, Yunjun Gao, Xiaoliang Xu, Lu Chen
6. VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents
   • arXiv 2024-10-14, https://arxiv.org/abs/2410.10594
   • Shi Yu, Chaoyue Tang, Bokai Xu, Junbo Cui, Junhao Ran, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun
   • TL;DR: Authors directly view web pages as images instead of spliting text or image information blocks. They use multi-modal retriever and Visual-understanding LLMs to build VisRAG framework and evaluate models on various visual QA tasks.
7. iRAG: Advancing RAG for Videos with an Incremental Approach
   • CIKM, 2024-10-21, https://dl.acm.org/doi/10.1145/3627673.3680088
   • Md Adnan Arefeen, Biplob Debnath, Md Yusuf Sarwar Uddin, Srimat Chakradhar
8. Video Enriched Retrieval Augmented Generation Using Aligned Video Captions
   • arXiv, 2024-5-27, https://arxiv.org/abs/2405.17706
   • Kevin Dela Rosa
9. Towards Retrieval Augmented Generation over Large Video Libraries
   • arXiv, 2024-6-21, https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10613524
   • Yannis Tevissen, Khalil Guetari, Frédéric Petitpont

📊 Benchmark

1. Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented Generation
   • arXiv, 2024-10-29, https://arxiv.org/abs/2410.21970
   • Suhang Wu, Jialong Tang, Baosong Yang, Ante Wang, Kaidi Jia, Jiawei Yu, Junfeng Yao, Jinsong Su

Conversational

1. RAC: Retrieval-augmented Conversation Dataset for Open-domain Question Answering in Conversational Settings
   • EMNLP 2024 Industry Track, https://aclanthology.org/2024.emnlp-industry.108
   • Bonggeun Choi, JeongJae Park, Yoonsung Kim, Jaehyun Park, Youngjoong Ko

Analysis

1. Long Context RAG Performance of Large Language Models
   • arxiv, 2024-11-5, https://arxiv.org/abs/2411.03538
   • Quinn Leng, Jacob Portes, Sam Havens, Matei Zaharia, Michael Carbin

🛠️ Toolkit

1. RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
   • arXiv, 2023-6-8, https://arxiv.org/abs/2306.05212
   • Jiongnan Liu, Jiajie Jin, Zihan Wang, Jiehan Cheng, Zhicheng Dou, Ji-Rong Wen