Q-Mirror: Unlocking the Multi-Modal Potential of Scientific Text-Only QA Pairs

Junying Wang, Zicheng Zhang, Ye Shen, Yalun Wu, Yingji Liang, Yijin Guo, Farong Wen, WenZhe Li, Xuezhi Zhao, Qi Jia, Guangtao Zhai

Keywords: Large Model, Text-Only QA Pairs, Multi-Modal QA Pairs

Abstract: High-quality, multi-modal benchmarks are crucial for advancing scientific reasoning in large models yet their manual creation is costly and unscalable. To address this bottleneck, we explore the potential for transforming Text-Only QA Pairs (TQAs) into high-quality Multi-Modal QA Pairs (MMQAs), which include three parts: 1) Task Definition & Evaluation Rubric: We develop a TQA-to-MMQA framework and establish a comprehensive, multi-dimensional MMQA quality rubric that provides principles for the transformation. 2) Benchmark Construction: Then we construct two extensive benchmarks to rigorously evaluate state-of-the-art generation & understanding models on the distinct tasks of MMQA generation & MMQA quality evaluation. 3) Preliminary Solution: We develop an agentic system Q-Mirror, which operationalizes our framework by integrating MMQA generation and evaluation into a closed loop for iterative refinement. Our experiments show that while state-of-the-art models can generate MMQAs, their outputs still leave substantial gaps, underscoring the need for reliable evaluation. We further demonstrate that top-tier understanding models align closely with human judgment in MMQA quality assessment. Leveraging both insights, the Q-Mirror agent raises average scores from 78.90 to 85.22 and pass rates from 72% to 95%, offering a practical path to large-scale scientific benchmarks.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 7248