标题： 从科学文献中自动提取网状材料合成细节的LLM支持的方法 显示英文标题

标题： Automated, LLM enabled extraction of synthesis details for reticular materials from scientific literature

摘要： 从科学文献中自动提取知识可以潜在地加速材料发现。 我们研究了一种使用大型语言模型（LLMs）从科学文献中提取网状材料合成工艺的方法。 为此，我们引入了一个知识提取管道（KEP），该管道自动化了LLM辅助的段落分类和信息提取。 通过将上下文学习（ICL）的提示工程应用于一组开源LLM，我们证明了LLM可以从PDF文档中检索化学信息，而无需微调或训练，并且幻觉风险较低。 通过比较五种开源LLM家族在段落分类和信息提取任务中的性能，我们观察到即使在ICL提示中仅包含少量示例段落，模型性能也非常出色。 结果表明，KEP方法在自动科学知识提取中具有减少人工标注和数据整理工作的潜力。 显示英文摘要

摘要： Automated knowledge extraction from scientific literature can potentially accelerate materials discovery. We have investigated an approach for extracting synthesis protocols for reticular materials from scientific literature using large language models (LLMs). To that end, we introduce a Knowledge Extraction Pipeline (KEP) that automatizes LLM-assisted paragraph classification and information extraction. By applying prompt engineering with in-context learning (ICL) to a set of open-source LLMs, we demonstrate that LLMs can retrieve chemical information from PDF documents, without the need for fine-tuning or training and at a reduced risk of hallucination. By comparing the performance of five open-source families of LLMs in both paragraph classification and information extraction tasks, we observe excellent model performance even if only few example paragraphs are included in the ICL prompts. The results show the potential of the KEP approach for reducing human annotations and data curation efforts in automated scientific knowledge extraction.