@inproceedings{li-etal-2025-hellaswag,
    title = "{H}ella{S}wag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of {LLM}s in Commonsense Reasoning",
    author = "Li, Xiaoyuan  and
      Li, Moxin  and
      Men, Rui  and
      Zhang, Yichang  and
      Bao, Keqin  and
      Wang, Wenjie  and
      Feng, Fuli  and
      Liu, Dayiheng  and
      Lin, Junyang",
    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.472/",
    pages = "9038--9072",
    ISBN = "979-8-89176-256-5",
    abstract = "Large language models (LLMs) have shown remarkable capabilities in commonsense reasoning; however, some variations in questions can trigger incorrect responses. Do these models truly understand commonsense knowledge, or just memorize expression patterns? To investigate this question, we present the first extensive robustness evaluation of LLMs in commonsense reasoning. We introduce HellaSwag-Pro, a large-scale bilingual benchmark consisting of 11,200 cases, by designing and compiling seven types of question variants. To construct this benchmark, we propose a two-stage method to develop Chinese HellaSwag, a finely annotated dataset comprising 12,000 instances across 56 categories. We conduct extensive experiments on 41 representative LLMs, revealing that these LLMs are far from robust in commonsense reasoning. Furthermore, this robustness varies depending on the language in which the LLM is tested. This work establishes a high-quality evaluation benchmark, with extensive experiments offering valuable insights to the community in commonsense reasoning for LLMs."
}