Exploring transformer models: Fine-tuning VS inference on relation extraction from biomedical texts.

ABSTRACT: Biomedical data continues to grow significantly, coming from different sources and being updated daily. This makes manual extraction not only time-consuming but also impossible to keep up with due to this constant increase. In this context, biomedical relation extraction, which aims to automate the discovery of relationships between entities from free texts, becomes an essential step for knowledge discovery. While fine-tuning Transformer models such as T5, PubMedBERT, BioBERT, ClinicalT5, and RoBERTa has shown satisfactory results, it requires specific datasets, which are time-consuming to create and costly since they require domain experts. One ideal solution is the use of Generative Artificial Intelligence (GenAI), as it is directly applicable to a problem without the need for data creat

SUBMITTER: El Janah H 

PROVIDER: S-EPMC12796933 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature