Leveraging Large Language Models for Cancer Vaccine Adjuvant Name Extraction from Biomedical Literature.

Abstract

This study explores cancer vaccine adjuvant name recognition using Large Language Models (LLMs), specifically Generative Pretrained Transformers (GPT), Large Language Model Meta AI (Llama), and Gemma. The models were tested in zero- and few-shot learning paradigms using AdjuvareDB and Vaccine Adjuvant Compendium (VAC) datasets. Prompts were designed to extract adjuvant names and assess the impact of contextual details. Notably, Llama-3.2 3B achieved a Recall of up to 68.7% (72.5% with manual validation) on the VAC dataset with four-shots, although its Precision and F1-score were lower. In contrast, GPT-4o, with additional contextual interventions, achieved a Precision of 65.9%, Recall of 79.7%, and F1-score of 69.8% on the AdjuvareDB dataset. Gemma-2 9B also demonstrated moderate few-shot gains, peaking at 63.6% F1-score. These LLMs outperformed BioBERT, a model widely used for biomedical text mining, highlighting the potential of general-purpose LLMs for automatic vaccine adjuvant name extraction and contributing to advancements in vaccine research.