Judge Model for Large-scale Multimodality Benchmarks

• URL: http://arxiv.org/abs/2601.06106v1
• Date: Sat, 03 Jan 2026 07:17:17 GMT
• Title: Judge Model for Large-scale Multimodality Benchmarks
• Authors: Min-Han Shih, Yu-Hsin Wu, Yu-Wei Chen,
• Abstract summary: We propose a dedicated multimodal Judge Model to provide reliable, explainable evaluation across a diverse suite of tasks.<n>Our framework aggregates multimodal judgments, analyzes the quality and reasoning consistency of model outputs, and generates diagnostic feedback.<n>Results show strong alignment between the Judge Model and human scores, demonstrating its potential as a scalable, interpretable evaluation pipeline.
• Score: 11.960445424565895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a dedicated multimodal Judge Model designed to provide reliable, explainable evaluation across a diverse suite of tasks. Our benchmark spans text, audio, image, and video modalities, drawing from carefully sampled public datasets with fixed seeds to ensure reproducibility and minimize train test leakage. Instead of simple scoring, our framework aggregates multimodal judgments, analyzes the quality and reasoning consistency of model outputs, and generates diagnostic feedback. We evaluate several MLLMs, including Gemini 2.5, Phi 4, and Qwen 2.5, across 280 multimodal samples and compare judge model assessments with human annotators. Results show strong alignment between the Judge Model and human scores, demonstrating its potential as a scalable, interpretable evaluation pipeline for future multimodal AI research.

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