@inproceedings{wang-etal-2025-mileage,
    title = "Your Mileage May Vary: How Empathy and Demographics Shape Human Preferences in {LLM} Responses",
    author = "Wang, Yishan  and
      Curry, Amanda Cercas  and
      Plaza-del-Arco, Flor Miriam",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.935/",
    doi = "10.18653/v1/2025.findings-emnlp.935",
    pages = "17257--17270",
    ISBN = "979-8-89176-335-7",
    abstract = "As large language models (LLMs) increasingly assist in subjective decision-making (e.g., moral reasoning, advice), it is critical to understand whose preferences they align with{---}and why. While prior work uses aggregate human judgments, demographic variation and its linguistic drivers remain underexplored. We present a comprehensive analysis of how demographic background and empathy level correlate with preferences for LLM-generated dilemma responses, alongside a systematic study of predictive linguistic features (e.g., agency, emotional tone). Our findings reveal significant demographic divides and identify markers (e.g., power verbs, tentative phrasing) that predict group-level differences. These results underscore the need for demographically informed LLM evaluation."
}