@inproceedings{rezaei-etal-2025-egonormia,
    title = "{E}go{N}ormia: Benchmarking Physical-Social Norm Understanding",
    author = "Rezaei, MohammadHossein  and
      Fu, Yicheng  and
      Cuvin, Phil  and
      Ziems, Caleb  and
      Zhang, Yanzhe  and
      Zhu, Hao  and
      Yang, Diyi",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.985/",
    pages = "19256--19283",
    ISBN = "979-8-89176-256-5",
    abstract = "Human activity is moderated by norms; however, supervision for normative reasoning is sparse, particularly where norms are physically- or socially-grounded. We thus present EgoNormia $\lVert \epsilon \rVert$, comprising 1,853 (200 for EgoNormia-verified) multiple choice questions (MCQs) grounded within ego-centric videos of human interactions, enabling the evaluation and improvement of normative reasoning in vision-language models (VLMs). spans seven norm categories: safety, privacy, proxemics, politeness, cooperation, coordination/proactivity, and communication/legibility. To compile this dataset at scale, we propose a novel pipeline to generate grounded MCQs from raw egocentric video. Our work demonstrates that current state-of-the-art VLMs lack robust grounded norm understanding, scoring a maximum of 54{\%} on EgoNormia and 58{\%} on EgoNormia-verified, with performance across norm categories indicating significant risks of safety and privacy when VLMs are used in real-world agents. We additionally explore methods for improving normative understanding, demonstrating a naive retrieval-based generation (RAG) method using can enhance normative reasoning in VLMs."
}