LEAP: LLM instruction-example adaptive prompting framework for biomedical relation extraction
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View abstract on PubMed
Summary
This summary is machine-generated.The LEAP framework enhances large language models (LLMs) for biomedical relation extraction by using adaptive prompting. This method improves performance over standard tuning, especially for complex data extraction tasks.
Area Of Science
- Biomedical informatics
- Natural Language Processing
- Artificial Intelligence
Background
- Large language models (LLMs) show promise for biomedical relation extraction.
- Current demonstration methods in LLMs require optimization for complex biomedical tasks.
- Adaptive tuning strategies are needed to enhance LLM performance in specialized domains.
Purpose Of The Study
- To investigate adaptive tuning methods for large language models (LLMs) in biomedical relation extraction.
- To introduce and evaluate the LLM instruction-example adaptive prompting (LEAP) framework.
- To assess the impact of adaptive task descriptions and examples on LLM performance.
Main Methods
- Analyzed demonstration components (task descriptions, examples) for LLMs in biomedical data tasks.
- Developed and implemented the LEAP framework with three adaptive tuning methods: instruction, example, and instruction-example adaptive tuning.
- Evaluated LEAP on DDI, ChemProt, and BioRED datasets using LLMs like Llama2 and MedLLaMA.
Main Results
- Instruction + Options + Example prompting significantly improved F1 scores compared to standard methods for zero-shot LLMs.
- The LEAP framework, particularly example adaptive prompting, outperformed conventional instruction tuning across all tested models.
- MedLLAMA_13B achieved a 95.13 F1 score on ChemProt, demonstrating robust performance on DDI and BioRED datasets.
Conclusions
- The LEAP framework provides an effective strategy for enhancing LLM training in biomedical relation extraction.
- This approach favors dynamic, contextually enriched prompting over extensive fine-tuning.
- LEAP offers a promising direction for improving LLM capabilities in sophisticated data extraction scenarios.
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