MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge

• URL: http://arxiv.org/abs/2604.18164v3
• Date: Thu, 23 Apr 2026 13:57:12 GMT
• Title: MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge
• Abstract summary: Multimodal Large Language Models (MLLMs) have been increasingly used as automatic evaluators-a paradigm known as MLLM-as-a-Judge.<n>We find that many MLLM judges fail to reliably integrate key visual or textual cues, yielding unreliable evaluations when evidence is missing or mismatched.<n> MM-JudgeBias introduces controlled perturbations across Query, Image, and Response, and evaluates model behavior via two complementary metrics: Bias-Deviation (BD) for sensitivity and Bias-Conformity (BC) for stability.
• Score: 6.343070599367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 via two complementary metrics: Bias-Deviation (BD) for sensitivity and Bias-Conformity (BC) for stability. Our dataset of over 1,800 curated and refined multimodal samples, drawn from 29 source benchmarks, enables a fine-grained diagnosis of nine bias types across diverse tasks and domains. Experiments on 26 state-of-the-art MLLMs reveal systematic modality neglect and asymmetric evaluation tendencies, underscoring the need for more reliable judges.

Related papers

• On the Adversarial Robustness of Multimodal LLM Judges [49.80009278197918]
  We introduce RobustMLLMJudge, the first framework for evaluating the adversarial robustness of general-purpose MLLMs when functioning as judges.<n>Using RobustMLLMJudge, we reveal that different MLLM judges are highly vulnerable to score-inflating adversarial attacks.<n>We propose MGSIA, a novel method that bypasses these constraints to enable more effective and transferable attacks on MLLM judges.
  arXiv  Detail & Related papers  (2026-06-14T05:30:20Z)
• Quantifying and Mitigating Self-Preference Bias of LLM Judges [5.017055831476582]
  Self-Preference Bias (SPB) is a directional evaluative deviation in which LLMs systematically favor or disfavor their own generated outputs during evaluation.<n>Existing measurements rely on costly human annotations and conflate generative capability with evaluative stance, and thus are impractical for large-scale deployment in real-world systems.<n>We introduce a fully automated framework to quantifying and mitigating SPB, which constructs equal-quality pairs of responses with negligible quality differences.
  arXiv  Detail & Related papers  (2026-04-24T09:46:22Z)
• Advancing Multimodal Judge Models through a Capability-Oriented Benchmark and MCTS-Driven Data Generation [20.16938320120462]
  M-JudgeBench is a capability-oriented benchmark to comprehensively assess the judgment abilities of MLLMs.<n>Judge-MCTS is a data construction framework generating pairwise reasoning trajectories with various correctness and length.<n>Our work establishes a more principled foundation for evaluating MLLM-as-a-judge through M-JudgeBench and Judge-MCTS framework.
  arXiv  Detail & Related papers  (2026-02-28T08:49:22Z)
• UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learning [101.62386137855704]
  We present a novel Universal Multimodal Embedding (UniME-V2) model.<n>Our approach first constructs a potential hard negative set through global retrieval.<n>We then introduce the MLLM-as-a-Judge mechanism, which utilizes MLLMs to assess the semantic alignment of query-candidate pairs.<n>These scores serve as a foundation for hard negative mining, mitigating the impact of false negatives and enabling the identification of diverse, high-quality hard negatives.
  arXiv  Detail & Related papers  (2025-10-15T13:07:00Z)
• TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them [58.04324690859212]
  Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks.<n>We identify two fundamental types of inconsistencies: Score-Comparison Inconsistency and Pairwise Transitivity Inconsistency.<n>We propose TrustJudge, a probabilistic framework that addresses these limitations through two key innovations.
  arXiv  Detail & Related papers  (2025-09-25T13:04:29Z)
• Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplifications and Resistance in Multi-Agent Based LLM-as-Judge [70.89799989428367]
  We conduct a systematic analysis of four diverse bias types: position bias, verbosity bias, chain-of-thought bias, and bandwagon bias.<n>We evaluate these biases across two widely adopted multi-agent LLM-as-Judge frameworks: Multi-Agent-Debate and LLM-as-Meta-Judge.
  arXiv  Detail & Related papers  (2025-05-26T03:56:41Z)
• Relative Bias: A Comparative Framework for Quantifying Bias in LLMs [29.112649816695203]
  Relative Bias is a method designed to assess how an LLM's behavior deviates from other LLMs within a specified target domain.<n>We introduce two complementary methodologies: (1) Embedding Transformation analysis, which captures relative bias patterns through sentence representations over the embedding space, and (2) LLM-as-a-Judge, which employs a language model to evaluate outputs comparatively.<n>Applying our framework to several case studies on bias and alignment scenarios following by statistical tests for validation, we find strong alignment between the two scoring methods.
  arXiv  Detail & Related papers  (2025-05-22T01:59:54Z)
• ProJudge: A Multi-Modal Multi-Discipline Benchmark and Instruction-Tuning Dataset for MLLM-based Process Judges [13.957207630090064]
  We introduce ProJudgeBench, the first benchmark specifically designed for evaluating abilities of MLLM-based process judges.<n>ProJudgeBench comprises 2,400 test cases and 50,118 step-level labels, spanning four scientific disciplines.<n> Evaluation on ProJudgeBench reveals a significant performance gap between open-source and proprietary models.
  arXiv  Detail & Related papers  (2025-03-09T10:55:51Z)
• Exploring Response Uncertainty in MLLMs: An Empirical Evaluation under Misleading Scenarios [49.53589774730807]
  Multimodal large language models (MLLMs) have recently achieved state-of-the-art performance on tasks ranging from visual question answering to video understanding.<n>We reveal a response uncertainty phenomenon: twelve state-of-the-art open-source MLLMs overturn a previously correct answer in 65% of cases after receiving a single deceptive cue.
  arXiv  Detail & Related papers  (2024-11-05T01:11:28Z)
• Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge [84.34545223897578]
  Despite their excellence in many domains, potential issues are under-explored, undermining their reliability and the scope of their utility. We identify 12 key potential biases and propose a new automated bias quantification framework-CALM- which quantifies and analyzes each type of bias in LLM-as-a-Judge. Our work highlights the need for stakeholders to address these issues and remind users to exercise caution in LLM-as-a-Judge applications.
  arXiv  Detail & Related papers  (2024-10-03T17:53:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.