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Plausible explanations mislead AI judges in multimodal models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new benchmark called JUPE to test the susceptibility of multimodal large language models (MLLMs) to misleading explanations. In experiments with 14 different MLLM judges, plausible but incorrect explanations significantly swayed the models' judgments, leading to a substantial increase in the acceptance of wrong answers. While Chain-of-Thought reasoning showed some mitigation, the study found that explanations can inflate performance estimates in evaluation pipelines, with current defenses offering limited protection. AI

IMPACT Highlights a vulnerability in AI judges that could inflate performance metrics, impacting model evaluation and development.

RANK_REASON Academic paper detailing a new benchmark and experimental findings on AI model behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Plausible explanations mislead AI judges in multimodal models

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Academic paper detailing a new benchmark and experimental findings on AI model behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Anglin Cai, Zhiqi Wang, Xiaozhi Yang, Yurun Lin, Dongwon Lee, Yuchen Yang ·

    A Plausible Explanation Is Enough to Mislead Multimodal Judges

    arXiv:2610.22631v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) increasingly serve as judges in training and evaluation pipelines, often assessing answers together with explanations. Although AI judges are sensitive to textual factors, it remains unclea…

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