@inproceedings{zhang-etal-2025-faithfulrag,
    title = "{F}aithful{RAG}: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation",
    author = "Zhang, Qinggang  and
      Xiang, Zhishang  and
      Xiao, Yilin  and
      Wang, Le  and
      Li, Junhui  and
      Wang, Xinrun  and
      Su, Jinsong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-long.1062/",
    pages = "21863--21882",
    ISBN = "979-8-89176-251-0",
    abstract = "Large language models (LLMs) augmented with retrieval systems have demonstrated significant potential in handling knowledge-intensive tasks. However, these models often struggle with unfaithfulness issues, generating outputs that either ignore the retrieved context or inconsistently blend it with the LLM{'}s parametric knowledge. This issue is particularly severe in cases of knowledge conflict, where the retrieved context conflicts with the model{'}s parametric knowledge. While existing faithful RAG approaches enforce strict context adherence through well-designed prompts or modified decoding strategies, our analysis reveals a critical limitation: they achieve faithfulness by forcibly suppressing the model{'}s parametric knowledge, which undermines the model{'}s internal knowledge structure and increases the risk of misinterpreting the context. To this end, this paper proposes FaithfulRAG, a novel framework that resolves knowledge conflicts by explicitly modeling discrepancies between the model{'}s parametric knowledge and retrieved context. Specifically, FaithfulRAG identifies conflicting knowledge at the fact level and designs a self-thinking process, allowing LLMs to reason about and integrate conflicting facts before generating responses. Extensive experiments demonstrate that our method outperforms state-of-the-art methods. The code is available at https://github.com/DeepLearnXMU/Faithful-RAG."
}