发表机构
The Hebrew University of Jerusalem; Allen Institute for AI (Ai2)（耶路撒冷希伯来大学; 艾伦人工智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究推出MUSE知识库，通过模块化抽取流程构建含3.7万个P-S-R三元组的跨领域资源，实验表明原理监督可提升复杂问题的性能但会损害简单问题的性能。
AI 中文摘要
科学论文包含了问题解决的细粒度记录：作者会提及技术障碍以及用来解决这些障碍的方法，通常还会说明选择这些方法的理由。我们推出MUSE（Mining Underlying Scientific Explanations，挖掘潜在科学解释），这是一个科学问题-解决方案-原理（P-S-R）三元组的全文多领域资源。我们整理了579篇经专家标注的全文段落，标注模式丰富，涵盖显著的问题、解决方案及原理片段，解决（solves）与原理归属（rationale_of）关系，以及概念共指。模块化抽取流程将该标注规模化，构建了一个包含3.7万个基于原文的P-S-R三元组的高质量知识库。我们对抽取组件进行评估，并开展了一项初步实验，训练了一个用于科学问题解决的原理监督型大语言模型（LLM）。有趣的是，我们发现原理监督能提升复杂多约束问题的性能，但会损害较简单问题的性能。
英文摘要
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.
