@inproceedings{li-etal-2026-born,
    title = "Born Pragmatic, Trained to Hallucinate? Quantifying the Origins of Contextual Bias in {LLM}s via the {P}a{CE} Benchmark",
    author = "Li, Ziming  and
      Tian, Yu  and
      Lan, Tian  and
      Li, Jiang  and
      Duo, Zehua  and
      Gao, Guanglai  and
      Su, Xiangdong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.959/",
    pages = "19211--19235",
    ISBN = "979-8-89176-395-1",
    abstract = "While Large Language Models (LLMs) excel at capturing communicative intent, this capability introduces a side effect: Pragmatic Hallucination, where models over-interpret literal contexts to generate non-factual inferences. To quantify this, we introduce the PaCE (Pragmatics-as-Context Evaluation) benchmark, comprising over 3,000 manually verified ``context-flip'' samples. Evaluations across nine mainstream models reveal a significant Context Sensitivity Gap (CSG), with literal accuracy consistently lagging behind pragmatic reasoning. Attribution analysis indicates that Reinforcement Learning from Human Feedback (RLHF) exacerbates this bias, and neither parameter scaling nor Chain-of-Thought (CoT) fully mitigates it. Crucially, ``Strict Prompting'' effectively reverses the CSG, demonstrating that the phenomenon stems from behavioral lock-in during training rather than inherent capability deficiencies. Furthermore, error patterns exhibit high systematic correlation across diverse architectures. This study highlights that current alignment paradigms lack precise control over pragmatic boundaries, underscoring the necessity for a ``Literal Grounding'' mechanism in future safety frameworks."
}