从全文文档中提取聚合物纳米复合材料样本
Extracting Polymer Nanocomposite Samples from Full-Length Documents
AI总结：
针对全文中聚合物纳米复合材料样本属性分散、实体标注困难的问题，本文构建新基准与评估方法，采用零样本提示和自洽性检验 LLM 抽取能力，并归纳三类错误挑战。
摘要：
本文研究了使用大型语言模型（LLM）从材料科学长篇研究论文中提取聚合物纳米复合材料（PNC）样本列表。挑战在于 PNC 样本的复杂性质，其众多属性分散在全文各处。标注 PNC 详细信息的复杂性限制了数据的可用性；由于难以创建全面的命名实体跨度标注，传统的文档级关系抽取技术并不实用。为解决这一问题，我们为该任务引入了一个新的基准和一种评估方法，并以零样本方式探索了不同的提示策略。我们还引入自洽性以提升性能。研究结果表明，即使是先进的 LLM 也难以从一篇文章中提取全部样本。最后，我们分析了此过程中遇到的错误，将其归纳为三大主要挑战，并讨论了未来研究克服这些挑战的潜在策略。
英文摘要：
This paper investigates the use of large language models (LLMs) for extracting sample lists of polymer nanocomposites (PNCs) from full-length materials science research papers. The challenge lies in the complex nature of PNC samples, which have numerous attributes scattered throughout the text. The complexity of annotating detailed information on PNCs limits the availability of data, making conventional document-level relation extraction techniques impractical due to the challenge in creating comprehensive named entity span annotations. To address this, we introduce a new benchmark and an evaluation technique for this task and explore different prompting strategies in a zero-shot manner. We also incorporate self-consistency to improve the performance. Our findings show that even advanced LLMs struggle to extract all of the samples from an article. Finally, we analyze the errors encountered in this process, categorizing them into three main challenges, and discuss potential strategies for future research to overcome them.
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。
