Too Polite to be Human: Evaluating LLM Empathy in K orean Conversations via a DCT -Based Framework (2025)

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.

## introduction with the rapid rise of large language models (llms), generating human-like text for tasks such as creative writing and ideation has become increasingly feasible. as a result, llms are now widely used in everyday conversations. however, despite the global popularity of english-centric models like gpt-4o and claude, they often fall short in capturing complex cues such as context, relationships, mood, and emotional nuance. this limitation becomes particularly salient in empathydriven interactions, where understanding goes beyond surface-level fluency. empathy is inherently shaped by sociocultural norms, requiring not only appropriate expression but also the accurate interpretation of social meaning @xcite @xcite . to examine this challenge, we evaluate llms' capacity for empathetic understanding in korean, a high-context language where relational nuance and social hierarchy are deeply embedded in linguistic form. korean's systematic use of politeness strategies, situated between japanese and chinese in terms of structural regularity @xcite , provides both linguistic richness and analytic control for our study. for evaluation, we introduce a pragmatics-based discourse completion task (dct) designed to assess llms' social judgment in empathetic scenarios. drawing on existing corpora, we construct a dataset of dialogue prompts that vary in key situational factors, including relational hierarchy, intimacy, emotional valence, and conversational context. this enables a fine-grained comparison of proprietary and open-source llms across diverse scenarios. figure 1 presents the dct structure used to probe llms' sociopragmatic reasoning. this study addresses four research questions, with key findings as follows: rq1: can llms empathize like humans? → most llms tended to over-empathize, using more intense expressions than humans. rq2: does model size or fine-tuning improve empathy? ## dataset construction to compare human and ai response patterns in empathetic dialogue, we derived two sub-datasets by reorganizing the existing dataset, korean empathetic dialogues (2022) from aihub. each sub-dataset comprises responses generated by three llms-gpt-4o, claude, and hyperclova-as well as native korean speakers. these responses were subsequently utilized to construct dct items for direct comparative analysis. the original corpus comprises dialogues with 14 to 16 turns, each annotated with emotional labels, relational roles, and situational contexts. these dialogues were reprocessed via two complementary strategies: (i) paraphrasing to enhance fluency and plausibility and (ii) retaining the original utterances for baseline comparison. in both versions, llms and human annotators generated empathetic responses while explicitly assuming the role of the empathizer. llms were provided with prompts specifying the target emotion, interpersonal relationship, and situational context. ## snunlp/kr-sbert-v40k-kluenli-augsts overall, llms exhibited lower response variability and a systematic bias toward longer responses, in contrast to the more balanced patterns observed in human participants. these tendencies highlight their limited grasp of the dynamics between hierarchy and intimacy, as well as their difficulty in adjusting empathic expression appropriately to contextual demands-particularly to response length. ## conclusions this study introduces a dct-based framework to evaluate the capacity of llms for socially appropriate empathy, focusing on interpretive judgment rather than generative fluency. by examining responses in korean, we reveal that llms often over-empathize or misread social cues, particularly in intimacy-based or relationally ambiguous contexts. contrary to expectations, model size and language-specific fine-tuning had minimal effect on performance. semantic and tf-idf analyses further show that human speakers modulate empathy based on subtle relational and emotional factors, whereas llms rely on surfacelevel patterns and reactive strategies. these findings underscore the need for llms to move beyond agent-like behavior and toward socially responsive communication. in particular, our framework can inform both the development of empathy-driven applications-such as virtual counseling or companionship-and the detection of manipulative misuse, where artificial empathy may be used to exploit users' trust in high-stakes settings such as voice phishing or persuasive dialogue. in this way, our study contributes to a broader understanding of how empathy should be calibrated, interpreted, and evaluated in socially deployed ai systems. the proposed dct framework is simple, flexible, and generalizable, offering a valuable foundation for future research on culturally grounded and socially competent ai. future work should address current limitations by incorporating more diverse social scenarios (e.g., teacher-student, workplace, stranger interactions) and extending the framework to multi-turn dialogues that better reflect the dynamics of real-world empathy. in addition, building datasets enriched with discourse-level features, such as relationship type, emotion cause, and social distance, will be crucial for developing models aligned with the sociocultural norms of high-context languages like korean.