一作NeurIPS 2026Poster
Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs
Nimet Beyza Bozdag · Emre Can Acikgoz · Gokhan Tur · Dilek Hakkani-Tur
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Nimet Beyza Bozdag · Emre Can Acikgoz · Gokhan Tur · Dilek Hakkani-Tur
Parisa Rabbani · Priyam Sahoo · Ruben Mathew · Aishee Mondal · Harshita Ketharaman · Nimet Beyza Bozdag · Dilek Hakkani-Tür
LLMs are increasingly used as third-party judges, yet their reliability when evaluating speakers in dialogue remains poorly understood. We show that LLMs judge identical claims differently depending on framing: the same content receives different verdicts when presented as a statement to verify (...
Pardis Sadat Zahraei · Xiaoning Wang · Nimet Beyza Bozdag · Gokhan Tur · Dilek Hakkani-Tür
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 co...