Bioregulatory event extraction using large language models: a case study of rice literature

Abstract

Abstract The extraction of biological regulation events has been a key focus in the field of biomedical nature language processing (BioNLP). However, existing methods often encounter challenges such as cascading errors in text mining pipelines and limitations in topic coverage from the selected corpus. Fortunately, the emergence of large language models (LLMs) presents a potential solution due to their robust semantic understanding and extensive knowledge base. To explore this potential, our project at the Biomedical Linked Annotation Hackathon 8 (BLAH 8) investigates the feasibility of using LLMs to extract biological regulation events. Our findings, based on the analysis of rice literature, demonstrate the promising performance of LLMs in this task, while also highlighting several concerns that must be addressed in future LLM-based application in low-resource topic.

Keywords

• Oryza sativa
• Bioregulatory event
• Text mining
• Large language model
• Prompt engineering