The Judge Can Debias Itself: Awareness and Self-Debiasing for Reliable LLM-as-a-Judge
LLM-as-a-Judge systems are increasingly employed for automated evaluation, yet remain susceptible to systematic biases that undermine their reliability. We demonstrate that the judge can effectively debias itself without additional training. We propose two lightweight strategies: bias-aware prompting, which explicitly alerts the judge to potential target biases prior to evaluation, and self-debiasing, in which the judge critiques the initial judgment for bias influence. To broaden the evaluation scope, we introduce three novel benchmarks--authority bias (fabricated citations), confidence bias (assertive phrasing), and formatting bias (structured presentation)--and evaluate across seven bi...
| Authors | Youbing Yin, Jingyu Wu, Anoop Kumar et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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