@inproceedings{aiyappa-etal-2023-trust,
    title = "Can we trust the evaluation on {C}hat{GPT}?",
    author = "Aiyappa, Rachith  and
      An, Jisun  and
      Kwak, Haewoon  and
      Ahn, Yong-yeol",
    editor = "Ovalle, Anaelia  and
      Chang, Kai-Wei  and
      Mehrabi, Ninareh  and
      Pruksachatkun, Yada  and
      Galystan, Aram  and
      Dhamala, Jwala  and
      Verma, Apurv  and
      Cao, Trista  and
      Kumar, Anoop  and
      Gupta, Rahul",
    booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.trustnlp-1.5",
    doi = "10.18653/v1/2023.trustnlp-1.5",
    pages = "47--54",
    abstract = "ChatGPT, the first large language model with mass adoption, has demonstrated remarkableperformance in numerous natural language tasks. Despite its evident usefulness, evaluatingChatGPT{'}s performance in diverse problem domains remains challenging due to the closednature of the model and its continuous updates via Reinforcement Learning from HumanFeedback (RLHF). We highlight the issue of data contamination in ChatGPT evaluations, with a case study in stance detection. We discuss the challenge of preventing data contamination and ensuring fair model evaluation in the age of closed and continuously trained models.",
}

ChatGPT, the first large language model with mass adoption, has demonstrated remarkableperformance in numerous natural language tasks. Despite its evident usefulness, evaluatingChatGPT{'}s performance in diverse problem domains remains challenging due to the closednature of the model and its continuous updates via Reinforcement Learning from HumanFeedback (RLHF). We highlight the issue of data contamination in ChatGPT evaluations, with a case study in stance detection. We discuss the challenge of preventing data contamination and ensuring fair model evaluation in the age of closed and continuously trained models.
Aiyappa, Rachith An, Jisun Kwak, Haewoon Ahn, Yong-yeol
Can we trust the evaluation on ChatGPT?