Are they lovers or friends? Evaluating LLMs' Social Reasoning in English
  and Korean Dialogues
Authors: name: Eunsu Kim, name: Junyeong Park, name: Juhyun Oh, name: Kiwoong Park, name: Seyoung Song, name: A. Seza Dogruoz, name: Najoung Kim, name: Alice Oh

As large language models (LLMs) are increasingly used in human-AI
interactions, their social reasoning capabilities in interpersonal contexts are
critical. We introduce SCRIPTS, a 1k-dialogue dataset in English and Korean,
sourced from movie scripts. The task involves evaluating models' social
reasoning capability to infer the interpersonal relationships (e.g., friends,
sisters, lovers) between speakers in each dialogue. Each dialogue is annotated
with probabilistic relational labels (Highly Likely, Less Likely, Unlikely) by
native (or equivalent) Korean and English speakers from Korea and the U.S.
Evaluating nine models on our task, current proprietary LLMs achieve around
75-80% on the English dataset, whereas their performance on Korean drops to
58-69%. More strikingly, models select Unlikely relationships in 10-25% of
their responses. Furthermore, we find that thinking models and chain-of-thought
prompting, effective for general reasoning, provide minimal benefits for social
reasoning and occasionally amplify social biases. Our findings reveal
significant limitations in current LLMs' social reasoning capabilities,
highlighting the need for efforts to develop socially-aware language models.