@inproceedings{li-etal-2025-c2leva,
    title = "{C}$^2${LEVA}: Toward Comprehensive and Contamination-Free Language Model Evaluation",
    author = "Li, Yanyang  and
      Long, Wong Tin  and
      Hung, Cheung To  and
      Zhao, Jianqiao  and
      Zheng, Duo  and
      Wai, Liu Ka  and
      Lyu, Michael R.  and
      Wang, Liwei",
    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/ingestion-acl-25/2025.findings-acl.116/",
    pages = "2283--2306",
    ISBN = "979-8-89176-256-5",
    abstract = "Recent advances in large language models (LLMs) have shown significant promise, yet their evaluation raises concerns, particularly regarding data contamination due to the lack of access to proprietary training data. To address this issue, we present C$^2$LEVA, a comprehensive bilingual benchmark featuring systematic contamination prevention. C$^2$LEVA firstly offers a holistic evaluation encompassing 22 tasks, each targeting a specific application or ability of LLMs, and secondly a trustworthy assessment due to our contamination-free tasks, ensured by a systematic contamination prevention strategy that fully automates test data renewal and enforces data protection during benchmark data release. Our large-scale evaluation of 15 open-source and proprietary models demonstrates the effectiveness of C$^2$LEVA."
}