@inproceedings{huangyw-etal-2025-dynamic,
    title = "Dynamic Attention-Guided Context Decoding for Mitigating Context Faithfulness Hallucinations in Large Language Models",
    author = "Huangyw, Huangyw  and
      Zhang, Yong  and
      Cheng, Ning  and
      Li, Zhitao  and
      Wang, Shaojun  and
      Xiao, Jing",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.269/",
    pages = "5174--5193",
    ISBN = "979-8-89176-256-5",
    abstract = "Large language models (LLMs) often exhibit Context Faithfulness Hallucinations, where outputs deviate from retrieved information due to incomplete context integration. Our analysis reveals a strong correlation between token-level uncertainty and hallucinations. We hypothesize that attention mechanisms inherently encode context utilization signals, supported by probing analysis. Based on these insights, we propose **Dynamic Attention-Guided Context Decoding (DAGCD)**, a lightweight framework that leverages attention distributions and uncertainty signals in a single-pass decoding. Experiments on open-book QA datasets demonstrate DAGCD{'}s effectiveness, yielding significant improvements in faithfulness and robustness while preserving computational efficiency."
}