Are they lovers or friends? Evaluating LLMs’ Social Reasoning in English and Korean Dialogues
Eunsu Kim, Junyeong Park Korea Advanced Institute of Science & Technology, Juhyun Oh Korea Advanced Institute of Science & Technology, Kiwoong Park Korea Advanced Institute of Science & Technology, Seyoung Song KAIST, A. Seza Doğruöz Ghent University, Alice Oh Google and Korea Advanced Institute of Science and Technology, Najoung Kim Boston University
As LLMs are increasingly deployed in real-world interactions, their social reasoning in interpersonal communication becomes critical. To explore their capabilities, we introduce SCRIPTS, a 1.1k-dialogue dataset in English and Korean, sourced from movie scripts and propose a social reasoning task based on SCRIPTS that evaluates the capacity of LLMs to infer the social relationships (e.g., friends, lovers) between speakers in each dialogue. Evaluating nine models on our task, current LLMs achieve around 75–80% on the English dataset and 58–69% in Korean, and models predict an Unlikely relationship in 10–25% of responses in both languages.Furthermore, we find that thinking models and chain-of-thought prompting provide minimal benefits for social reasoning and occasionally amplify social biases.In sum, there are significant limitations in current LLMs’ social reasoning capabilities, especially for Korean, highlighting the need for efforts to develop socially-aware LLMs across languages.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表