@inproceedings{park-etal-2025-polite,
    title = "Too Polite to be Human: Evaluating {LLM} Empathy in {K}orean Conversations via a {DCT}-Based Framework",
    author = "Park, Seoyoon  and
      Kim, Jaehee  and
      Kim, Hansaem",
    editor = "Hale, James  and
      Deuksin Kwon, Brian  and
      Dutt, Ritam",
    booktitle = "Proceedings of the Third Workshop on Social Influence in Conversations (SICon 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.sicon-1.6/",
    pages = "76--89",
    ISBN = "979-8-89176-266-4",
    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."
}