BiasScope: Towards Automated Detection of Bias in LLM-as-a-Judge Evaluation

• URL: http://arxiv.org/abs/2602.09383v1
• Date: Tue, 10 Feb 2026 03:51:03 GMT
• Title: BiasScope: Towards Automated Detection of Bias in LLM-as-a-Judge Evaluation
• Authors: Peng Lai, Zhihao Ou, Yong Wang, Longyue Wang, Jian Yang, Yun Chen, Guanhua Chen,
• Abstract summary: BiasScope is a framework for automatically and at scale discovering potential biases during model evaluation.<n>JudgeBench-Pro is a more challenging benchmark for evaluating the robustness of LLM-as-a-judge.
• Score: 31.32711926164484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM-as-a-Judge has been widely adopted across various research and practical applications, yet the robustness and reliability of its evaluation remain a critical issue. A core challenge it faces is bias, which has primarily been studied in terms of known biases and their impact on evaluation outcomes, while automated and systematic exploration of potential unknown biases is still lacking. Nevertheless, such exploration is crucial for enhancing the robustness and reliability of evaluations. To bridge this gap, we propose BiasScope, a LLM-driven framework for automatically and at scale discovering potential biases that may arise during model evaluation. BiasScope can uncover potential biases across different model families and scales, with its generality and effectiveness validated on the JudgeBench dataset. It overcomes the limitations of existing approaches, transforming bias discovery from a passive process relying on manual effort and predefined bias lists into an active and comprehensive automated exploration. Moreover, based on BiasScope, we propose JudgeBench-Pro, an extended version of JudgeBench and a more challenging benchmark for evaluating the robustness of LLM-as-a-judge. Strikingly, even powerful LLMs as evaluators show error rates above 50\% on JudgeBench-Pro, underscoring the urgent need to strengthen evaluation robustness and to mitigate potential biases further.

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