@inproceedings{purkayastha-etal-2026-common,
    title = "Common Sense vs. Morality: The Curious Case of Narrative Focus Bias in {LLM}s",
    author = "Purkayastha, Saugata  and
      Kushare, Pranav  and
      Pal, Pragya Paramita  and
      Purkayastha, Sukannya",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/test-year-match/2026.lrec-1.835/",
    doi = "10.63317/23hsqksy9475",
    pages = "10653--10663",
    abstract = "Large Language Models (LLMs) are increasingly deployed across diverse real-world applications and user communities. As such, it is crucial that these models remain both morally grounded and knowledge-aware. In this work, we uncover a critical limitation of current LLMs{---}their tendency to prioritize moral reasoning over commonsense understanding. To investigate this phenomenon, we introduce \textsc{CoMoral}, a novel benchmark dataset containing commonsense contradictions embedded within moral dilemmas. Through extensive evaluation of ten LLMs across different model sizes, we find that existing models consistently struggle to identify such contradictions without prior signal. Furthermore, we observe a pervasive \textit{narrative focus} bias, wherein LLMs more readily detect commonsense contradictions when they are attributed to a secondary character rather than the primary (narrator) character. Our comprehensive analysis underscores the need for enhanced reasoning-aware training to improve the commonsense robustness of large language models."
}