AI RESEARCH
Quantifying and Mitigating Self-Preference Bias of LLM Judges
arXiv CS.LG
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ArXi:2604.22891v1 Announce Type: new LLM-as-a-Judge has become a dominant approach in automated evaluation systems, playing critical roles in model alignment, leaderboard construction, quality control, and so on. However, the scalability and trustworthiness of this approach can be substantially distorted by Self-Preference Bias (SPB), which is a directional evaluative deviation in which LLMs systematically favor or disfavor their own generated outputs during evaluation.