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arXiv AI By Songeun Chae, Min Kim, Donghoon Jung, Seojin Choi, Seohyon Jung

Self- and Other-Labels Induce Bidirectional Bias in LLM Judges

Read the original on arXiv AI →

The study investigates bias in large language model (LLM) judges by having ten LLMs evaluate narrative constraint selections rather than generated text. Results show that self-preference largely disappears under blind evaluation when quality and evaluator severity are controlled, but self- and other-labels alone shift scores bidirectionally when quality is matched. The authors conclude that authorship attribution drives evaluation bias and that open-ended, ground‑truth‑free tasks can effectively study LLM judge behavior.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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