构建科学论文中的问题-解法-理由三元组知识库，助力智能科研推理。
MUSE: A Full-Text Cross-Domain Knowledge Base of Scientific Problems, Solutions, and Rationales
- 从全文提取问题、解法与理由三元组，支持跨领域科学知识挖掘。
- 构建包含3.7万条源文本支撑的高质量知识库，专家标注579段文本。
- 用理由监督训练大模型，复杂问题性能提升，简单问题反而下降。
科学论文包含精细的问题求解记录：作者常提及技术障碍及其解决方法，并附带选择该方法的理由。我们提出MUSE（Mining Underlying Scientific Explanations），一个覆盖多领域的全文本科学问题-解法-理由（P-S-R）三元组资源。我们精心标注了579段专家评审的完整文本段落，采用丰富标注模式，涵盖关键问题、解法与理由片段，并建立解决关系和概念指代关系。通过模块化抽取流程，将标注规模扩展至3.7万条来源可信的P-S-R三元组。我们评估了各抽取组件性能，并初步实验了以理由监督训练的大语言模型在科学问题求解中的表现。有趣的是，理由监督能提升复杂多约束问题的性能，但在简单问题上反而降低效果。
原文摘要 · Abstract (English)
Scientific papers contain fine-grained records of problem solving: authors mention technical obstacles and methods that were used to address them, often along with reasoning on why those methods were chosen. We introduce MUSE (Mining Underlying Scientific Explanations), a full-text, multi-domain resource of scientific Problem-Solution-Rationale (P-S-R) triplets. We curate 579 expert-annotated full-text paragraphs, with a rich annotation schema covering salient problem, solution, and rationale spans, solves and rationale_of links and conceptual coreference. A modular extraction pipeline scales this annotation to build a high-quality knowledge base of 37K source-grounded P-S-R triplets. We evaluate the extraction components and include a preliminary experiment training a rationale-supervised LLM for scientific problem solving. Interestingly, we find that rationale supervision improves performance on complex, multi-constraint problems but can harm performance on simpler ones.
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