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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

PLRTE: Progressive learning for biomedical relation triplet extraction using large language models.

Yi-Kai Zheng¹, Bi Zeng², Yi-Chun Feng³

¹School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510000, China; Guangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou 510005, China.

Journal of Biomedical Informatics

|October 19, 2024

Summary

This summary is machine-generated.

This study introduces a progressive learning strategy (PLRTE) to improve biomedical relation triplet extraction models. The novel approach enhances model generalization for drug discovery and knowledge graph construction.

Keywords:

Biomedical text mining Large language model Named entity recognition Natural language processing Relation triplet extraction Supervised fine-tuning

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Area of Science:

Biomedical Informatics
Natural Language Processing
Computational Biology

Background:

Document-level relation triplet extraction is vital for biomedical knowledge discovery.
Current models struggle with generalization to new datasets and relation types.

Purpose of the Study:

To enhance the generalization capabilities of biomedical relation triplet extraction models.
To optimize models by focusing on data-task relevance and relation granularity.

Main Methods:

Introduced a novel progressive learning strategy to develop the PLRTE model.
Implemented a four-level progressive learning process: semantic relation augmentation, compositional instruction, and dual-axis level learning.

Main Results:

Achieved 5% to 20% performance improvement over state-of-the-art baselines on DDI and BC5CDR datasets.
Demonstrated exceptional generalization on unseen Chemprot and GDA datasets.

Conclusions:

Optimizing data-task association and relation granularity significantly enhances model generalizability.
The PLRTE model shows strong potential for advancing biomedical text mining applications.