SciMDR：推动科学多模态文档推理
SciMDR: Advancing Scientific Multimodal Document Reasoning
AI总结：
本文提出SciMDR数据集，通过合成与重嵌框架生成大规模多模态文档推理数据，提升科学问答任务的复杂度和真实性。
摘要：
构建科学多模态文档推理数据集以训练基础模型涉及规模、忠实度和真实性的权衡。为解决这一挑战，我们引入合成与重嵌框架，该框架包含两个阶段：（1）以主张为中心的问答合成，生成忠实且隔离的问答对并在聚焦段落上进行推理；（2）文档级重嵌，通过程序性重新嵌入这些对到完整文档任务中以确保真实复杂性。使用此框架，我们构建了SciMDR，一个大规模跨模态理解训练数据集，包含30万对具有显式推理链的问答对，覆盖2万个科学论文。我们进一步构建了SciMDR-Eval，一个专家标注的基准，用于评估多模态理解在完整科学流程中的表现。实验表明，经过SciMDR微调的模型在多个科学问答基准上取得显著提升，特别是在需要复杂文档级推理的任务中。
英文摘要：
Constructing scientific multimodal document reasoning datasets for foundation model training involves an inherent trade-off among scale, faithfulness, and realism. To address this challenge, we introduce the synthesize-and-reground framework, a two-stage pipeline comprising: (1) Claim-Centric QA Synthesis, which generates faithful, isolated QA pairs and reasoning on focused segments, and (2) Document-Scale Regrounding, which programmatically re-embeds these pairs into full-document tasks to ensure realistic complexity. Using this framework, we construct SciMDR, a large-scale training dataset for cross-modal comprehension, comprising 300K QA pairs with explicit reasoning chains across 20K scientific papers. We further construct SciMDR-Eval, an expert-annotated benchmark to evaluate multimodal comprehension within full-length scientific workflows. Experiments demonstrate that models fine-tuned on SciMDR achieve significant improvements across multiple scientific QA benchmarks, particularly in those tasks requiring complex document-level reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
