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        <title>Too Polite to be Human: Evaluating LLM Empathy in Korean Conversations via a DCT-Based Framework</title>
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    <abstract>As LLMs are increasingly used in global conversational settings, concerns remain about their ability to handle complex sociocultural contexts. This study evaluates LLMs’ empathetic understanding in Korean—a high-context language—using a pragmatics-based Discourse Completion Task (DCT) focused on interpretive judgment rather than generation. We constructed a dataset varying relational hierarchy, intimacy, and emotional valence, and compared responses from proprietary and open-source LLMs to those of Korean speakers. Most LLMs showed over-empathizing tendencies and struggled with ambiguous relational cues. Neither model size nor Korean fine-tuning significantly improved performance. While humans reflected relational nuance and contextual awareness, LLMs relied on surface strategies. These findings underscore LLMs’ limits in socio-pragmatic reasoning and introduce a scalable, culturally flexible framework for evaluating socially-aware AI.</abstract>
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%0 Conference Proceedings
%T Too Polite to be Human: Evaluating LLM Empathy in Korean Conversations via a DCT-Based Framework
%A Park, Seoyoon
%A Kim, Jaehee
%A Kim, Hansaem
%Y Hale, James
%Y Kwon, Brian Deuksin
%Y Dutt, Ritam
%S Proceedings of the Third Workshop on Social Influence in Conversations (SICon 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-266-4
%F park-etal-2025-polite
%X As LLMs are increasingly used in global conversational settings, concerns remain about their ability to handle complex sociocultural contexts. This study evaluates LLMs’ empathetic understanding in Korean—a high-context language—using a pragmatics-based Discourse Completion Task (DCT) focused on interpretive judgment rather than generation. We constructed a dataset varying relational hierarchy, intimacy, and emotional valence, and compared responses from proprietary and open-source LLMs to those of Korean speakers. Most LLMs showed over-empathizing tendencies and struggled with ambiguous relational cues. Neither model size nor Korean fine-tuning significantly improved performance. While humans reflected relational nuance and contextual awareness, LLMs relied on surface strategies. These findings underscore LLMs’ limits in socio-pragmatic reasoning and introduce a scalable, culturally flexible framework for evaluating socially-aware AI.
%R 10.18653/v1/2025.sicon-1.6
%U https://aclanthology.org/2025.sicon-1.6/
%U https://doi.org/10.18653/v1/2025.sicon-1.6
%P 76-89

Markdown (Informal)

[Too Polite to be Human: Evaluating LLM Empathy in Korean Conversations via a DCT-Based Framework](https://aclanthology.org/2025.sicon-1.6/) (Park et al., SICon 2025)

• Too Polite to be Human: Evaluating LLM Empathy in Korean Conversations via a DCT-Based Framework (Park et al., SICon 2025)

ACL

• Seoyoon Park, Jaehee Kim, and Hansaem Kim. 2025. Too Polite to be Human: Evaluating LLM Empathy in Korean Conversations via a DCT-Based Framework. In Proceedings of the Third Workshop on Social Influence in Conversations (SICon 2025), pages 76–89, Vienna, Austria. Association for Computational Linguistics.