@inproceedings{jeon-etal-2025-going,
    title = "``Going to a trap house'' conveys more fear than ``Going to a mall'': Benchmarking Emotion Context Sensitivity for {LLM}s",
    author = "Jeon, Eojin  and
      Lee, Mingyu  and
      Kim, Sangyun  and
      Kim, Junho  and
      Cho, Wanzee  and
      Kam, Tae-Eui  and
      Lee, SangKeun",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.802/",
    doi = "10.18653/v1/2025.findings-emnlp.802",
    pages = "14848--14869",
    ISBN = "979-8-89176-335-7",
    abstract = "Emotion context sensitivity{---}the ability to adjust emotional responses based on contexts{---}is a core component of human emotional intelligence. For example, being told, ``You can come with me if you want,'' may elicit joy if the destination is a mall, but provoke fear if the destination is a trap house. As large language models (LLMs) are increasingly deployed in socially interactive settings, understanding this human ability becomes crucial for generating context-appropriate, emotion-aware responses. In this work, we introduce Trace, a novel benchmark for evaluating whether LLMs can understand emotion context sensitivity of humans. This benchmark consists of 1,626 social scenarios and comprises two complementary tests: a sensitivity test, which measures whether models can detect emotional shifts caused by context changes, and a robustness test, which evaluates whether models can maintain stable emotion predictions when context changes are emotionally irrelevant. Each scenario pair keeps the core event constant while systematically varying contextual details{---}time, place, or agent{---}based on insights from behavioral theory and emotion psychology. Experimental results show that even the best-performing LLMs lag behind human performance by 20{\%} in the sensitivity test and 15{\%} in the robustness test, indicating substantial room for improvement in emotion-aware reasoning."
}