Self- and Other-Labels Induce Bidirectional Bias in LLM Judges
As LLM-as-a-judge becomes increasingly widespread, self-preference -- the tendency of a judge to favor its own outputs -- raises growing concerns about evaluation reliability. However, this bias has been studied predominantly on generated text, where stylistic features and response quality are inevitably conflated. As a result, existing measurements cannot separate genuine self-preference from these confounds. We address this limitation by changing the object of evaluation: instead of judging generated text, ten LLMs assess sets of narrative constraints selected from a shared pool, which carry no stylistic fingerprint yet retain a recoverable model-specific signature. Two experiments on t...
| Authors | Songeun Chae, Min Kim, Donghoon Jung et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 June 2026 |
| Last updated at source | 6 September 2026 |
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