Objective: To develop a high-throughput biomedical relation extraction system that takes advantage of the large language models' (LLMs) reading comprehension ability and biomedical world knowledge in a scalable and evidential manner. Methods: We formulate the relation extraction task as a simple binary classification problem for large language models such as ChatGPT. Specifically, LLMs make the decision based on the external corpus and its world knowledge, giving the reason for the judgment to factual verification. This method is tailored for semi-structured web articles, wherein we designate the main title as the tail entity and explicitly incorporate it into the context, and the potential head entities are matched based on a biomedical thesaurus. Moreover, lengthy contents are sliced into text chunks, embedded, and retrieved with additional embedding models, ensuring compatibility with the context window size constraints of available open-source LLMs. Results: Using an open-source LLM, we extracted 304315 relation triplets of three distinct relation types from four reputable biomedical websites. To assess the efficacy of the basic pipeline employed for biomedical relation extraction, we curated a benchmark dataset annotated by a medical expert. Evaluation results indicate that the pipeline exhibits performance comparable to that of GPT-4. Case studies further illuminate challenges faced by contemporary LLMs in the context of biomedical relation extraction for semi-structured web articles. Conclusion: The proposed method has demonstrated its effectiveness in leveraging the strengths of LLMs for high-throughput biomedical relation extraction. Its adaptability is evident, as it can be seamlessly extended to diverse semi-structured biomedical websites, facilitating the extraction of various types of biomedical relations with ease.

翻译：目的：开发一种高吞吐生物医学关系抽取系统，利用大语言模型的阅读理解能力和生物医学世界知识，以可扩展且可验证的方式实现。方法：我们将关系抽取任务简化为针对ChatGPT等大语言模型的二元分类问题。具体而言，大语言模型基于外部语料库及其世界知识做出决策，并给出判断理由以支持事实验证。该方法专为半结构化网络文章设计，我们将主标题指定为尾实体并显式融入上下文，同时基于生物医学词表匹配潜在头实体。此外，长文本被切分为文本块，通过额外嵌入模型进行嵌入与检索，以确保兼容开源大语言模型的上下文窗口大小限制。结果：使用开源大语言模型，我们从四个权威生物医学网站中提取了三种不同关系类型的304,315个关系三元组。为评估基础管线在生物医学关系抽取中的效能，我们构建了由医学专家标注的基准数据集。评估结果表明，该管线的性能与GPT-4相当。案例研究进一步揭示了当前大语言模型在处理半结构化网络文章的生物医学关系抽取时面临的挑战。结论：所提方法在利用大语言模型优势实现高吞吐生物医学关系抽取方面展现出有效性。其适应性显著，可无缝扩展至各类半结构化生物医学网站，轻松促进多种生物医学关系的抽取。