Biomedical triple extraction systems aim to automatically extract biomedical entities and relations between entities. The exploration of applying large language models (LLM) to triple extraction is still relatively unexplored. In this work, we mainly focus on sentence-level biomedical triple extraction. Furthermore, the absence of a high-quality biomedical triple extraction dataset impedes the progress in developing robust triple extraction systems. To address these challenges, initially, we compare the performance of various large language models. Additionally, we present GIT, an expert-annotated biomedical triple extraction dataset that covers a wider range of relation types.
@article{arxiv.2310.18463,
title = {Benchingmaking Large Langage Models in Biomedical Triple Extraction},
author = {Mingchen Li and Huixue Zhou and Rui Zhang},
journal= {arXiv preprint arXiv:2310.18463},
year = {2024}
}