@inproceedings{plaza-del-arco-etal-2025-yes,
    title = "No for Some, Yes for Others: Persona Prompts and Other Sources of False Refusal in Language Models",
    author = {Plaza-del-Arco, Flor Miriam  and
      R{\"o}ttger, Paul  and
      Scherrer, Nino  and
      Borgonovo, Emanuele  and
      Plischke, Elmar  and
      Hovy, Dirk},
    editor = "Zhang, Chen  and
      Allaway, Emily  and
      Shen, Hua  and
      Miculicich, Lesly  and
      Li, Yinqiao  and
      M'hamdi, Meryem  and
      Limkonchotiwat, Peerat  and
      Bai, Richard He  and
      T.y.s.s., Santosh  and
      Han, Sophia Simeng  and
      Thapa, Surendrabikram  and
      Rim, Wiem Ben",
    booktitle = "Proceedings of the 9th Widening NLP Workshop",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.winlp-main.39/",
    pages = "268--282",
    ISBN = "979-8-89176-351-7",
    abstract = "Large language models (LLMs) are increasingly integrated into our daily lives and personalized. However, LLM personalization might also increase unintended side effects. Recent work suggests that persona prompting can lead models to falsely refuse user requests. However, no work has fully quantified the extent of this issue. To address this gap, we measure the impact of 15 sociodemographic personas (based on gender, race, religion, and disability) on false refusal. To control for other factors, we also test 16 different models, 3 tasks (Natural Language Inference, politeness, and offensiveness classification), and nine prompt paraphrases. We propose a Monte Carlo-based method to quantify this issue in a sample-efficient manner. Our results show that as models become more capable, personas impact the refusal rate less. However, we find that the choice of model significantly influence false refusals, especially in sensitive content tasks. The impact of certain sociodemographic personas further increases the false refusal effect in some models, which suggests that there are underlying biases in the alignment strategies or safety mechanisms."
}