@inproceedings{li-etal-2025-llms-trust,
    title = "{LLM}s Trust Humans More, That{'}s a Problem! Unveiling and Mitigating the Authority Bias in Retrieval-Augmented Generation",
    author = "Li, Yuxuan  and
      Guo, Xinwei  and
      Gao, Jiashi  and
      Chen, Guanhua  and
      Zhao, Xiangyu  and
      Zhang, Jiaxin  and
      Liu, Quanying  and
      Wu, Haiyan  and
      Yao, Xin  and
      Wei, Xuetao",
    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.1400/",
    pages = "28844--28858",
    ISBN = "979-8-89176-251-0",
    abstract = "Retrieval-Augmented Generation (RAG) has been proven to be an effective approach to address the hallucination problem in large language models (LLMs). In current RAG systems, LLMs typically need to synthesize knowledge provided by two main external sources (user prompts and an external database) to generate a final answer. When the knowledge provided by the user conflicts with that retrieved from the database, a critical question arises: Does the LLM favor one knowledge source over the other when generating the answer? In this paper, we are the first to unveil a new phenomenon, Authority Bias, where the LLMs tend to favor the knowledge provided by the user even when it deviates from the facts; this new phenomenon is rigorously evidenced via our novel and comprehensive characterization of Authority Bias in six widely used LLMs and across diverse task scenarios. We propose a novel dataset specifically designed for detecting Authority Bias, called the Authority Bias Detection Dataset (ABDD), and introduce new, detailed metrics to measure Authority Bias. To mitigate Authority bias, we finally propose the Conflict Detection Enhanced Query (CDEQ) framework. We identify the sentences and atomic information that generate conflicts, perform a credibility assessment on the conflicting paragraphs, and ultimately enhance the query to detect perturbed text, thereby reducing Authority bias. Comparative experiments with widely used mitigation methods demonstrate that CDEQ exhibits both effectiveness and advancement, significantly enhancing the robustness of RAG systems."
}