The rapid expansion of chemistry literature poses significant challenges for researchers seeking to efficiently access domain-specific knowledge. To support advancements in chemistry-focused natural language processing (NLP), we present ChemRxivQuest, a curated dataset of 970 high-quality question-answer (QA) pairs derived from 155 ChemRxiv preprints across 17 subfields of chemistry. Each QA pair is explicitly linked to its source text segment to ensure traceability and contextual accuracy. ChemRxivQuest was constructed using an automated pipeline that combines optical character recognition (OCR), GPT-4o-based QA generation, and a fuzzy matching technique for answer verification. The dataset emphasizes conceptual, mechanistic, applied, and experimental questions, enabling applications in retrieval-based QA systems, search engine development, and fine-tuning of domain-adapted large language models. We analyze the dataset's structure, coverage, and limitations, and outline future directions for expansion and expert validation. ChemRxivQuest provides a foundational resource for chemistry NLP research, education, and tool development.

本研究针对化学文献快速扩展所带来的信息获取挑战，提出了ChemRxivQuest，一个来自155篇ChemRxiv预印本的970对高质量问答的策划数据库。该数据库强调了概念性、机制性、应用性和实验性问题，为检索式问答系统及领域适应的大型语言模型微调提供了重要的基础资源。

ChemRxivQuest：从ChemRxiv预印本提取的策划化化学问答数据库