@inproceedings{zahraei-etal-2026-prior,
    title = "Prior Beliefs Prejudice {LLM}-as-Judge: Evidence from Persuasion Evaluation",
    author = "Zahraei, Pardis Sadat  and
      Wang, Xiaoning  and
      Bozdag, Nimet Beyza  and
      Tur, Gokhan  and
      Hakkani-T{\textbackslash}{''}ur, Dilek",
    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.2087/",
    pages = "42049--42082",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) are increasingly used as judges to evaluate text quality, moderate content, and assess arguments. We investigate whether alignment-instilled prior beliefs bias LLM judgments, using persuasion evaluation as a representative task. We find a systematic failure: models conflate their trained beliefs with rhetorical quality, rating identical claims differently based on belief alignment rather than argumentative merit. A bare assertion aligned with training receives higher scores than a well-crafted counter-argument, even when explicitly instructed to judge rhetoric alone. We introduce ConvinceQA, a dataset of 27,756 persuasive arguments with controlled stance variation across subjective, harmful, and misinformation domains, and demonstrate this prior prejudice across models. We exploit this failure through persuasion-based probing: evaluating minimal pairs that differ only in the subject token bypasses learned refusals and reveals hidden biases. Analysis identifies three failure modes, with belief-conditioned rating inflation accounting for 88{\%} of cases. Cross-task validation on essay quality assessment and debate judging confirms this is a pervasive limitation."
}