@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Your Mileage May Vary: How Empathy and Demographics Shape Human Preferences in LLM Responses
%A Wang, Yishan
%A Curry, Amanda Cercas
%A Plaza-del-Arco, Flor Miriam
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F wang-etal-2025-mileage
%X 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.
%R 10.18653/v1/2025.findings-emnlp.935
%U https://aclanthology.org/2025.findings-emnlp.935/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.935
%P 17257-17270
Markdown (Informal)
[Your Mileage May Vary: How Empathy and Demographics Shape Human Preferences in LLM Responses](https://aclanthology.org/2025.findings-emnlp.935/) (Wang et al., Findings 2025)
ACL