@inproceedings{tian-etal-2026-know,
title = "Know Your Place: Diagnosing Implicit Social Adaptation Failures in {C}hinese Large Language Models",
author = "Tian, Yu and
Xing, Jie and
Li, Ziming and
Li, Jiang and
Duo, Zehua and
Lan, Tian and
Liu, Xu and
Gao, Guanglai and
Su, Xiangdong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1148/",
pages = "25030--25062",
ISBN = "979-8-89176-390-6",
abstract = "As large language models (LLMs) are increasingly deployed in dialogue systems and interactive agents, their social adaptation during natural interaction has drawn growing attention. While prior work shows strong social regulation under explicit role or style instructions, it remains unclear whether LLMs can spontaneously perceive and respond to implicit social differences without explicit prompts. Focusing on high-context Chinese interactions, we identify a robust phenomenon termed Social Agnosia, where LLMs fail to adequately perceive and accommodate implicit social power, affective arousal, and epistemic status during natural interaction. To diagnose this behavior, we propose C-ISA, a framework grounded in Communication Accommodation Theory that decomposes social adaptation into three approximately orthogonal dimensions, and conduct controlled comparisons across multiple Chinese LLMs under implicit and explicit conditions. Results show that while models substantially adjust linguistic strategies under explicit conditioning, they exhibit socially insensitive and homogenized responses in natural interaction, revealing a structural gap between spontaneous behavior and conditioned capability. The C-ISA dataset is publicly available at https://github.com/ty373/C-ISA."
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%0 Conference Proceedings
%T Know Your Place: Diagnosing Implicit Social Adaptation Failures in Chinese Large Language Models
%A Tian, Yu
%A Xing, Jie
%A Li, Ziming
%A Li, Jiang
%A Duo, Zehua
%A Lan, Tian
%A Liu, Xu
%A Gao, Guanglai
%A Su, Xiangdong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F tian-etal-2026-know
%X As large language models (LLMs) are increasingly deployed in dialogue systems and interactive agents, their social adaptation during natural interaction has drawn growing attention. While prior work shows strong social regulation under explicit role or style instructions, it remains unclear whether LLMs can spontaneously perceive and respond to implicit social differences without explicit prompts. Focusing on high-context Chinese interactions, we identify a robust phenomenon termed Social Agnosia, where LLMs fail to adequately perceive and accommodate implicit social power, affective arousal, and epistemic status during natural interaction. To diagnose this behavior, we propose C-ISA, a framework grounded in Communication Accommodation Theory that decomposes social adaptation into three approximately orthogonal dimensions, and conduct controlled comparisons across multiple Chinese LLMs under implicit and explicit conditions. Results show that while models substantially adjust linguistic strategies under explicit conditioning, they exhibit socially insensitive and homogenized responses in natural interaction, revealing a structural gap between spontaneous behavior and conditioned capability. The C-ISA dataset is publicly available at https://github.com/ty373/C-ISA.
%U https://aclanthology.org/2026.acl-long.1148/
%P 25030-25062
Markdown (Informal)
[Know Your Place: Diagnosing Implicit Social Adaptation Failures in Chinese Large Language Models](https://aclanthology.org/2026.acl-long.1148/) (Tian et al., ACL 2026)
ACL
- Yu Tian, Jie Xing, Ziming Li, Jiang Li, Zehua Duo, Tian Lan, Xu Liu, Guanglai Gao, and Xiangdong Su. 2026. Know Your Place: Diagnosing Implicit Social Adaptation Failures in Chinese Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 25030–25062, San Diego, California, United States. Association for Computational Linguistics.