@inproceedings{tang-etal-2025-cognibench,
    title = "{C}ogni{B}ench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language Models",
    author = "Tang, Xiaqiang  and
      Li, Jian  and
      Hu, Keyu  and
      Du, Nan  and
      Li, Xiaolong  and
      Zhang, Xi  and
      Sun, Weigao  and
      Xie, Sihong",
    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/ingestion-acl-25/2025.acl-long.1046/",
    pages = "21567--21585",
    ISBN = "979-8-89176-251-0",
    abstract = "Faithfulness hallucinations are claims generated by a Large Language Model (LLM) not supported by contexts provided to the LLM. Lacking assessment standards, existing benchmarks focus on ``factual statements'' that rephrase source materials while overlooking ``cognitive statements'' that involve making inferences from the given context. Consequently, evaluating and detecting the hallucination of cognitive statements remains challenging. Inspired by how evidence is assessed in the legal domain, we design a rigorous framework to assess different levels of faithfulness of cognitive statements and introduce the CogniBench dataset where we reveal insightful statistics. To keep pace with rapidly evolving LLMs, we further develop an automatic annotation pipeline that scales easily across different models. This results in a large-scale CogniBench-L dataset, which facilitates training accurate detectors for both factual and cognitive hallucinations. We release our model and datasets at: https://github.com/FUTUREEEEEE/CogniBench"
}