Relation extraction using large language models: a case study on acupuncture point locations.

ABSTRACT:

Objective

In acupuncture therapy, the accurate location of acupoints is essential for its effectiveness. The advanced language understanding capabilities of large language models (LLMs) like Generative Pre-trained Transformers (GPTs) and Llama present a significant opportunity for extracting relations related to acupoint locations from textual knowledge sources. This study aims to explore the performance of LLMs in extracting acupoint-related location relations and assess the impact of fine-tuning on GPT's performance.

Materials and methods

We utilized the World Health Organization Standard Acupuncture Point Locations in the Western Pacific Region (WHO Standard) as our corpus, which consists of descriptions of 361 acupoints. Five types of relations ("direction_of", "distance

SUBMITTER: Li Y 

PROVIDER: S-EPMC11491641 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature