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Related Concept Videos

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Related Experiment Video

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LLM Instruction-Example Adaptive Prompting (LEAP) Framework for Clinical Relation Extraction.

Huixue Zhou1, Mingchen Li2, Yongkang Xiao1

  • 1Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.

Medrxiv : the Preprint Server for Health Sciences
|January 3, 2024
PubMed
Summary
This summary is machine-generated.

The Instruction-Example Adaptive Prompting (LEAP) framework enhances Large Language Models (LLMs) for clinical relation extraction. Example adaptive prompting within LEAP significantly improves performance over traditional methods.

Keywords:
Clinical Relation ExtractionInstruction TuningInstruction-Example Adaptive PromptingLarge Language Model

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Area of Science:

  • Natural Language Processing
  • Machine Learning

Background:

  • Clinical relation extraction is crucial for biomedical research and drug discovery.
  • Large Language Models (LLMs) show potential but require effective prompting strategies for complex tasks.
  • Adaptive prompting methods aim to improve LLM performance without extensive fine-tuning.

Approach:

  • Introduced the Instruction-Example Adaptive Prompting (LEAP) framework.
  • Investigated the impact of adaptive task descriptions and examples in LLM demonstrations.
  • Evaluated LEAP on DDI and BC5CDR datasets using models like Llama2 and MedLLaMA.

Key Points:

  • Combining instructions, options, and examples significantly boosted F1-scores compared to standard prompting.
  • Example adaptive prompting within LEAP outperformed traditional instruction tuning across tested LLMs.
  • MedLLaMA-13B achieved a 95.13 F1-score on BC5CDR using LEAP's example adaptive prompting.

Conclusions:

  • The LEAP framework offers a dynamic and contextually rich alternative to extensive LLM fine-tuning.
  • Adaptive prompting strategies show promise for advancing clinical relation extraction with LLMs.
  • LEAP demonstrates robustness and effectiveness across different datasets and LLM architectures.