@inproceedings{chen-etal-2026-large,
title = "Do Large Language Models Adapt Politeness Like Humans?",
author = "Chen, Tianfang and
Yao, Yuyang and
Tang, Liping",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.669/",
pages = "15412--15429",
ISBN = "979-8-89176-459-0",
abstract = "Polite language production involves balancing strategic success with local convention, but these pressures are difficult to separate in natural interaction. We therefore examine them in an artificial-language request game, where one marker increased request acceptance and another appeared more often in other speakers' requests. Humans and LLMs completed the same task under goals to maximize acceptance, match local usage, or produce a natural and appropriate request. For both humans and LLMs, a policy-mixture model combining payoff- and exposure-sensitive policies provided the best overall predictive fit. However, their baseline behavior differed. Human baseline behavior fell between the strategy and convention conditions, suggesting sensitivity to both payoff and local usage. LLMs shifted toward local usage under the convention goal, but their baselines remained closer to the strategy condition. These results show that human and LLM behavior is sensitive to both payoff and local usage in polite production, but that LLM baselines are more payoff-oriented. Code, data, and materials are all available at https://github.com/CHENTianfang/emnlp2026-politeness."
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<abstract>Polite language production involves balancing strategic success with local convention, but these pressures are difficult to separate in natural interaction. We therefore examine them in an artificial-language request game, where one marker increased request acceptance and another appeared more often in other speakers’ requests. Humans and LLMs completed the same task under goals to maximize acceptance, match local usage, or produce a natural and appropriate request. For both humans and LLMs, a policy-mixture model combining payoff- and exposure-sensitive policies provided the best overall predictive fit. However, their baseline behavior differed. Human baseline behavior fell between the strategy and convention conditions, suggesting sensitivity to both payoff and local usage. LLMs shifted toward local usage under the convention goal, but their baselines remained closer to the strategy condition. These results show that human and LLM behavior is sensitive to both payoff and local usage in polite production, but that LLM baselines are more payoff-oriented. Code, data, and materials are all available at https://github.com/CHENTianfang/emnlp2026-politeness.</abstract>
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%0 Conference Proceedings
%T Do Large Language Models Adapt Politeness Like Humans?
%A Chen, Tianfang
%A Yao, Yuyang
%A Tang, Liping
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F chen-etal-2026-large
%X Polite language production involves balancing strategic success with local convention, but these pressures are difficult to separate in natural interaction. We therefore examine them in an artificial-language request game, where one marker increased request acceptance and another appeared more often in other speakers’ requests. Humans and LLMs completed the same task under goals to maximize acceptance, match local usage, or produce a natural and appropriate request. For both humans and LLMs, a policy-mixture model combining payoff- and exposure-sensitive policies provided the best overall predictive fit. However, their baseline behavior differed. Human baseline behavior fell between the strategy and convention conditions, suggesting sensitivity to both payoff and local usage. LLMs shifted toward local usage under the convention goal, but their baselines remained closer to the strategy condition. These results show that human and LLM behavior is sensitive to both payoff and local usage in polite production, but that LLM baselines are more payoff-oriented. Code, data, and materials are all available at https://github.com/CHENTianfang/emnlp2026-politeness.
%U https://aclanthology.org/2026.emnlp-main.669/
%P 15412-15429
Markdown (Informal)
[Do Large Language Models Adapt Politeness Like Humans?](https://aclanthology.org/2026.emnlp-main.669/) (Chen et al., EMNLP 2026)
ACL
- Tianfang Chen, Yuyang Yao, and Liping Tang. 2026. Do Large Language Models Adapt Politeness Like Humans?. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15412–15429, Budapest, Hungary. Association for Computational Linguistics.