@inproceedings{chehbouni-etal-2025-beyond,
    title = "Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset",
    author = "Chehbouni, Khaoula  and
      Carr, Jonathan Cola{\c{c}}o  and
      More, Yash  and
      Cheung, Jackie CK  and
      Farnadi, Golnoosh",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.596/",
    pages = "11895--11925",
    ISBN = "979-8-89176-189-6",
    abstract = "In an effort to mitigate the harms of large language models (LLMs), learning from human feedback (LHF) has been used to steer LLMs towards outputs that are intended to be both less harmful and more helpful. Despite the widespread adoption of LHF in practice, the quality of this feedback and its effectiveness as a safety mitigation technique remain unclear. This study addresses these issues by auditing the widely-used Helpful and Harmless (HH) dataset by Anthropic. Our work includes: (1) a thorough investigation of the dataset{'}s content through both manual and automated evaluation; (2) experiments demonstrating the dataset{'}s impact on models' safety; and (3) an analysis of the 100 most influential papers citing this dataset. Through our audit, we showcase how conceptualization failures and quality issues identified in the HH dataset can create additional harms by leading to disparate safety behaviors across demographic groups. Our findings highlight the need for more nuanced, context-sensitive approaches to safety mitigation in LLMs."
}