@inproceedings{fan-etal-2026-feeling,
    title = "Feeling Rules in Language Models: Mapping Norms of Emotional Appropriateness Across Roles, Institutions, and Intensity",
    author = "Fan, Guangrui  and
      Liu, DanDan  and
      Sabri, Aznul Qalid MD  and
      Zhang, Rui  and
      Lihu, Pan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.462/",
    pages = "10175--10193",
    ISBN = "979-8-89176-390-6",
    abstract = "When asked explicitly, a Large language model (LLM) may validate your anger{---}but implicitly, it may still judge that anger as inappropriate. We call this divergence the endorsement{--}exposure gap, and it reveals that LLMs encode hidden norms about which emotions are acceptable in which contexts. To measure these norms systematically, we introduce Feeling Rules Atlas, a benchmark of 1,320 vignettes spanning 6 institutional settings, 12 roles, 7 emotions, and 5 intensity levels. We pair the benchmark with two probes: explicit norm judgments (APPROPRIATE/INAPPROPRIATE/DEPENDS) and implicit acceptability scored by log-likelihood contrast. Across six model families, we find large cross-model variation in sanctioning thresholds and institutional ``norm signatures'' not reducible to overall strictness; models that appear similarly lenient explicitly can diverge sharply in implicit judgments. These results establish normative affect{---}context-conditioned judgments of emotional appropriateness{---}as a distinct alignment axis, and motivate transparent profiling of feeling rules for emotionally sensitive deployments."
}