Benchingmaking Large Langage Models in Biomedical Triple Extraction

• URL: http://arxiv.org/abs/2310.18463v6
• Date: Sat, 27 Apr 2024 13:28:18 GMT
• Title: Benchingmaking Large Langage Models in Biomedical Triple Extraction
• Authors: Mingchen Li, Huixue Zhou, Rui Zhang,
• Abstract summary: This work mainly focuses on sentence-level biomedical triple extraction. The absence of a high-quality biomedical triple extraction dataset impedes the progress in developing robust triple extraction systems. We present GIT, an expert-annotated biomedical triple extraction dataset.
• Score: 13.022101126299269
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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