Large Language Models for Pathway Curation: A Preliminary Investigation

Abstract

The pathway curation task involves analyzing scientific literature to identify and represent cellular processes as pathways. This process, often time-consuming and labor-intensive, requires significant curation efforts amidst the rapidly growing biomedical literature. Natural Language Processing (NLP) offers a promising method to automatically extract these interactions from scientific texts. Despite immense progress, there remains room for improvement in these systems. The emergence of Large Language Models (LLMs) provides a promising solution for this challenge. Our study conducts a preliminary investigation into leveraging LLMs for the pathway curation task. This paper first presents a review of the current state-of-the-art algorithms for the pathway curation task. Our objective is to check the feasibility and formulate strategies of using these LLMs to improve the accuracy of pathway curation task. Our experiments demonstrate that our GPT-3.5 based fine-tuned models outperforms existing state-of-the-art methods. Specifically, our model achieved a 10 basis point improvement in over-all recall and F1 score compared to the best existing algorithms. These findings highlight the potential of LLMs in pathway curation tasks, warranting further research and substantial efforts in this direction. Keypoints/ObjectivesO_LIStudy evaluates the feasibility of using Large Language Models (LLMs) for pathway curation in scientific literature. C_LIO_LIUsing GPT-3.5 based fine tuned models for pathway curation, we compare its performance with existing methods, focusing on precision, recall and F1 score metrics. C_LIO_LIEmphasize the promise and need for further research on using LLMs for pathway curation. C_LI