mlsanityData to 14 Oct 2026

Benchingmaking Large Langage Models in Biomedical Triple Extraction

arXiv 2310.18463 ↗First posted 27 Oct 2023cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 16 of its 52 references that are in the map. First posted on arXiv 27 Oct 2023.

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16 of 52 references.

References · 16

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  9. 2022UniRel: Unified Representation and Interaction for Joint Relational Triple Extractioncited by 14
  10. 2023How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domaincited by 14
  11. 2022Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graphcited by 9
  12. 2022A Hierarchical N-Gram Framework for Zero-Shot Link Predictioncited by 9
4 more
  1. 2023RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extractioncited by 7
  2. 2022Query-based Instance Discrimination Network for Relational Triple Extractioncited by 5
  3. 2023Easy-to-Hard Learning for Information Extractioncited by 5
  4. 2020Multi-Fusion Chinese WordNet (MCW) : Compound of Machine Learning and Manual Correctioncited by 4

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2025MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learningcited by 13
  2. 2024Benchmarking Retrieval-Augmented Large Language Models in Biomedical NLP: Application, Robustness, and Self-Awarenesscited by 11