@inproceedings{zhu-etal-2025-rationales,
    title = "Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability",
    author = "Zhu, Chiwei  and
      Xu, Benfeng  and
      Yang, An  and
      Lin, Junyang  and
      Wang, Quan  and
      Zhou, Chang  and
      Mao, Zhendong",
    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.302/",
    pages = "5808--5835",
    ISBN = "979-8-89176-256-5",
    abstract = "Training language models with rationales augmentation has been shown to be beneficial in many existing works. In this paper, we identify that such a prevailing view does not hold consistently. We conduct comprehensive investigations to thoroughly inspect the impact of rationales on model performance as well as a novel perspective of model reliability. The results lead to several key findings that add new insights upon existing understandings: 1) Rationales can, at times, deteriorate model performance; 2) Rationales can, at times, improve model reliability, even outperforming their untrained counterparts; 3) A linear correspondence exists in between the performance and reliability improvements, while both are driven by the intrinsic difficulty of the task. These findings provide informative regulations on the broad utilization of rationales and raise critical implications on the procedure of explicitly aligning language models with implicit human thoughts. Codes can be found in this anonymous link: https://anonymous.4open.science/r/rationales-CEE8."
}