Systematic Evaluation of Evaluation Protocol Factors Driving Extreme Qwen2.5 Performance Discrepancies on DocVQA
Abstract
Document Visual Question Answering (DocVQA) faces dual challenges in processing lengthy multimodal documents (text, images, tables) and performing cross-modal reasoning. Current document retrieval-augmented generation (DocRAG) methods remain limited by their text-centric approaches, frequently missing critical visual information. The field also lacks robust benchmarks for assessing multimodal evidence selection and integration. We introduce MMDocRAG, a comprehensive benchmark featuring 4,055 expert-annotated QA pairs with multi-page, cross-modal evidence chains. Our framework introduces innova Research goal: Reproducibility meta-analysis: 3 independent publications report divergent Qwen2.5 performance on Docvqa with a 80.3 percentage-point spread (range 14.1%–94.3%). Source papers: "DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections" (2025, 14.1%); "VisionSelector: End-to-End Learnable Visual Token Compression for Efficient Mul…" (2025, 94.3%); "VisionSelector: End-to-End Learnable Visual Token Compression for Efficient Mul…" (2025, 94.3%). Preliminary analysis suggests: The extreme discrepancy likely stems from DocHop-QA evaluating Qwen2.5 in a strict zero-shot setting on complex multi-hop reasoning tasks without fine-tuning, whereas VisionSelector reports scores from a model checkpoint that has been fine-tuned or augmented with their specific visual token compression module. Additio… Systematically evaluate which evaluation protocol factors (model…
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