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Evidence record 5413 · automatically gathered

Quantifying and Mitigating Self-Preference Bias of LLM Judges

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. Existing measurements rely on costly human annotations and con

Record details

Published: 24 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (24 April 2026), “Quantifying and Mitigating Self-Preference Bias of LLM Judges,” evidence record 5413, https://ethics.ai/record/5413 (originally published by arXiv).

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