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.

利用大型语言模型的阅读理解能力和生物医学领域知识，开发了一个高通量的生物医学关系提取系统，将关系提取任务作为一个简单的二分类问题，通过外部语料库和世界知识进行判断，并从可靠医学网站中提取多种类型的生物医学关系。