@inproceedings{zhao-etal-2024-longrag,
    title = "{L}ong{RAG}: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering",
    author = "Zhao, Qingfei  and
      Wang, Ruobing  and
      Cen, Yukuo  and
      Zha, Daren  and
      Tan, Shicheng  and
      Dong, Yuxiao  and
      Tang, Jie",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-main.1259/",
    doi = "10.18653/v1/2024.emnlp-main.1259",
    pages = "22600--22632",
    abstract = "Long-Context Question Answering (LCQA), a challenging task, aims to reason over long-context documents to yield accurate answers to questions. Existing long-context Large Language Models (LLMs) for LCQA often struggle with the {\textquotedblleft}lost in the middle{\textquotedblright} issue. Retrieval-Augmented Generation (RAG) mitigates this issue by providing external factual evidence. However, its chunking strategy disrupts the global long-context information, and its low-quality retrieval in long contexts hinders LLMs from identifying effective factual details due to substantial noise. To this end, we propose LongRAG, a general, dual-perspective, and robust LLM-based RAG system paradigm for LCQA to enhance RAG`s understanding of complex long-context knowledge (i.e., global information and factual details). We design LongRAG as a plug-and-play paradigm, facilitating adaptation to various domains and LLMs. Extensive experiments on three multi-hop datasets demonstrate that LongRAG significantly outperforms long-context LLMs (up by 6.94{\%}), advanced RAG (up by 6.16{\%}), and Vanilla RAG (up by 17.25{\%}). Furthermore, we conduct quantitative ablation studies and multi-dimensional analyses, highlighting the effectiveness of the system`s components and fine-tuning strategies.Data and code are available at [https://github.com/QingFei1/LongRAG](https://github.com/QingFei1/LongRAG)."
}