MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge
Multimodal Large Language Models (MLLMs) have been increasingly used as automatic evaluators-a paradigm known as MLLM-as-a-Judge. However, their reliability and vulnerabilities to biases remain underexplored. We find that many MLLM judges fail to reliably integrate key visual or textual cues, yielding unreliable evaluations when evidence is missing or mismatched, and exhibiting instability under semantically irrelevant perturbations. To address this, we systematically define Compositional Bias in MLLM-as-a-Judge systems and introduce MM-JudgeBias, a benchmark for evaluating it. MM-JudgeBias introduces controlled perturbations across Query, Image, and Response, and evaluates model behavior...
| Authors | Sua Lee, Sanghee Park, Jinbae Im |
|---|---|
| Primary category | cs.CL |
| Published | 20 April 2026 |
| Last updated at source | 23 April 2026 |
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