@inproceedings{lum-etal-2025-bias,
    title = "Bias in Language Models: Beyond Trick Tests and Towards {RUTE}d Evaluation",
    author = "Lum, Kristian  and
      Anthis, Jacy Reese  and
      Robinson, Kevin  and
      Nagpal, Chirag  and
      D{'}Amour, Alexander Nicholas",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-long.7/",
    pages = "137--161",
    ISBN = "979-8-89176-251-0",
    abstract = "Standard bias benchmarks used for large language models (LLMs) measure the association between social attributes in model inputs and single-word model outputs. We test whether these benchmarks are robust to lengthening the model outputs via a more realistic user prompt, in the commonly studied domain of gender-occupation bias, as a step towards measuring Realistic Use and Tangible Effects (i.e., RUTEd evaluations). From the current literature, we adapt three standard metrics of next-word prediction (neutrality, skew, and stereotype), and we develop analogous RUTEd evaluations in three contexts of real-world LLM use: children{'}s bedtime stories, user personas, and English language learning exercises. We find that standard bias metrics have no significant correlation with long-form output metrics. For example, selecting the least biased model based on the standard ``trick tests'' coincides with selecting the least biased model based on longer output no more than random chance. There may not yet be evidence to justify standard benchmarks as reliable proxies of real-world biases, and we encourage further development of context-specific RUTEd evaluations."
}