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arXiv · 2506.21604

Evaluating VisualRAG: Quantifying Cross-Modal Performance in Enterprise Document Understanding

Varun Mannam·Fang Wang·Xin Chen
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Abstract

Current evaluation frameworks for multimodal generative AI struggle to establish trustworthiness, hindering enterprise adoption where reliability is paramount. We introduce a systematic, quantitative benchmarking framework to measure the trustworthiness of progressively integrating cross-modal inputs such as text, images, captions, and OCR within VisualRAG systems for enterprise document intelligence. Our approach establishes quantitative relationships between technical metrics and user-centric trust measures. Evaluation reveals that optimal modality weighting with weights of 30% text, 15% image, 25% caption, and 30% OCR improves performance by 57.3% over text-only baselines while maintaining computational efficiency. We provide comparative assessments of foundation models, demonstrating their differential impact on trustworthiness in caption generation and OCR extraction-a vital consideration for reliable enterprise AI. This work advances responsible AI deployment by providing a rigorous framework for quantifying and enhancing trustworthiness in multimodal RAG for critical enterprise applications.

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Varun Mannam, Fang Wang, Xin Chen. 2025-06-19. Evaluating VisualRAG: Quantifying Cross-Modal Performance in Enterprise Document Understanding. https://arxiv.org/abs/2506.21604

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