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Retrieval-Augmented Generation (RAG)

Here're some resources about Retrieval-Augmented Generation with LLMs

Method

VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents

tag: VisRAG | Tsinghua University

paper link: here

github link: here

citation:

@misc{yu2024visragvisionbasedretrievalaugmentedgeneration,
      title={VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents}, 
      author={Shi Yu and Chaoyue Tang and Bokai Xu and Junbo Cui and Junhao Ran and Yukun Yan and Zhenghao Liu and Shuo Wang and Xu Han and Zhiyuan Liu and Maosong Sun},
      year={2024},
      eprint={2410.10594},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2410.10594}, 
}

LightRAG: Simple and Fast Retrieval-Augmented Generation

tag: LightRAG | HKU

paper link: here

github link: here

citation:

@misc{guo2024lightragsimplefastretrievalaugmented,
      title={LightRAG: Simple and Fast Retrieval-Augmented Generation}, 
      author={Zirui Guo and Lianghao Xia and Yanhua Yu and Tu Ao and Chao Huang},
      year={2024},
      eprint={2410.05779},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2410.05779}, 
}

MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery

tag: MemoRAG | BAAI

paper link: here

github link: here

citation:

@misc{qian2024memoragmovingnextgenrag,
      title={MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery}, 
      author={Hongjin Qian and Peitian Zhang and Zheng Liu and Kelong Mao and Zhicheng Dou},
      year={2024},
      eprint={2409.05591},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.05591}, 
}

RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation

tag: RAGLAB | EMNLP24 | Nanjing University

paper link: here

github link: here

citation:

@misc{zhang2024raglabmodularresearchorientedunified,
      title={RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation}, 
      author={Xuanwang Zhang and Yunze Song and Yidong Wang and Shuyun Tang and Xinfeng Li and Zhengran Zeng and Zhen Wu and Wei Ye and Wenyuan Xu and Yue Zhang and Xinyu Dai and Shikun Zhang and Qingsong Wen},
      year={2024},
      eprint={2408.11381},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2408.11381}, 
}

LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs

tag: LongRAG | University of Waterloo

paper link: here

github link: here

homepage link: here

citation:

@misc{jiang2024longragenhancingretrievalaugmentedgeneration,
      title={LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs}, 
      author={Ziyan Jiang and Xueguang Ma and Wenhu Chen},
      year={2024},
      eprint={2406.15319},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.15319}, 
}

From Local to Global: A Graph RAG Approach to Query-Focused Summarization

tag: GraphRAG | Microsoft

paper link: here

blog link: here

github link: here

doc link: here

homepage link: here

citation:

@misc{edge2024localglobalgraphrag,
      title={From Local to Global: A Graph RAG Approach to Query-Focused Summarization}, 
      author={Darren Edge and Ha Trinh and Newman Cheng and Joshua Bradley and Alex Chao and Apurva Mody and Steven Truitt and Jonathan Larson},
      year={2024},
      eprint={2404.16130},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2404.16130}, 
}

RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

tag: RQ-RAG | HKU

paper link: here

github link: here

citation:

@misc{chan2024rqraglearningrefinequeries,
      title={RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation}, 
      author={Chi-Min Chan and Chunpu Xu and Ruibin Yuan and Hongyin Luo and Wei Xue and Yike Guo and Jie Fu},
      year={2024},
      eprint={2404.00610},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2404.00610}, 
}

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

tag: Self-RAG | Allen AI

paper link: here

github link: here

homepage link: here

citation:

@misc{asai2023selfraglearningretrievegenerate,
      title={Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection}, 
      author={Akari Asai and Zeqiu Wu and Yizhong Wang and Avirup Sil and Hannaneh Hajishirzi},
      year={2023},
      eprint={2310.11511},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2310.11511}, 
}

Precise Zero-Shot Dense Retrieval without Relevance Labels

tag: HyDE | CMU

paper link: here

github link: here

citation:

@misc{gao2022precise,
      title={Precise Zero-Shot Dense Retrieval without Relevance Labels}, 
      author={Luyu Gao and Xueguang Ma and Jimmy Lin and Jamie Callan},
      year={2022},
      eprint={2212.10496},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}

Generate rather than retrieve: Large language models are strong context generators

tag: GenRead | ICLR23 | Microsoft

paper link: here

github link: here

citation:

@article{yu2022generate,
  title={Generate rather than retrieve: Large language models are strong context generators},
  author={Yu, Wenhao and Iter, Dan and Wang, Shuohang and Xu, Yichong and Ju, Mingxuan and Sanyal, Soumya and Zhu, Chenguang and Zeng, Michael and Jiang, Meng},
  journal={arXiv preprint arXiv:2209.10063},
  year={2022}
}

Retrieval-augmented generation for knowledge-intensive nlp tasks

tag: RAG | NIPS20 | Meta

paper link: here

citation:

@article{lewis2020retrieval,
  title={Retrieval-augmented generation for knowledge-intensive nlp tasks},
  author={Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and K{\"u}ttler, Heinrich and Lewis, Mike and Yih, Wen-tau and Rockt{\"a}schel, Tim and others},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={9459--9474},
  year={2020}
}

Benchmark

Benchmarking Large Language Models in Retrieval-Augmented Generation

tag: RGB | ISCAS

paper link: here

github link: here

citation:

@misc{chen2023benchmarking,
      title={Benchmarking Large Language Models in Retrieval-Augmented Generation}, 
      author={Jiawei Chen and Hongyu Lin and Xianpei Han and Le Sun},
      year={2023},
      eprint={2309.01431},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Survey

Graph Retrieval-Augmented Generation: A Survey

tag: GraphRAG Survey | Peking University

paper link: here

github link: here

citation:

@misc{peng2024graphretrievalaugmentedgenerationsurvey,
      title={Graph Retrieval-Augmented Generation: A Survey}, 
      author={Boci Peng and Yun Zhu and Yongchao Liu and Xiaohe Bo and Haizhou Shi and Chuntao Hong and Yan Zhang and Siliang Tang},
      year={2024},
      eprint={2408.08921},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2408.08921}, 
}

Retrieval-Augmented Generation for Large Language Models: A Survey

tag: RAG Survey | Tongji University

paper link: here

github link: here

citation:

@misc{gao2024retrievalaugmented,
      title={Retrieval-Augmented Generation for Large Language Models: A Survey}, 
      author={Yunfan Gao and Yun Xiong and Xinyu Gao and Kangxiang Jia and Jinliu Pan and Yuxi Bi and Yi Dai and Jiawei Sun and Qianyu Guo and Meng Wang and Haofen Wang},
      year={2024},
      eprint={2312.10997},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}