🤖 AI Summary
This work addresses the lack of reliability, interpretability, and consistency analysis in existing automated evaluation methods for multimodal tasks. To this end, it proposes the first interpretable evaluation framework tailored for large-scale multimodal benchmarks, introducing a dedicated multimodal judge model that integrates textual, audio, visual, and video signals. Rather than relying solely on scalar scores, the framework generates diagnostic feedback through reasoning consistency analysis. Leveraging a multimodal large language model architecture and publicly available datasets with fixed-seed sampling, the method evaluates multiple state-of-the-art models on 280 samples. The results demonstrate strong alignment with human annotations, confirming the framework’s reliability, scalability, and comprehensive capability in assessing the output quality of multimodal foundation models.
📝 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.