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  11. Harnessing Large Language Models' Zero-shot And Few-shot Learning Capabilities For Regulatory Research

Harnessing large language models' zero-shot and few-shot learning capabilities for regulatory research

Hamed Meshkin1, Joel Zirkle1, Ghazal Arabidarrehdor1

  • 1Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64, 10903 New Hampshire Ave, Silver Spring, MD 20993, United States.

Briefings in Bioinformatics
|August 23, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

Open-source large language models (LLMs) can be deployed locally for secure data processing. These models demonstrate strong performance in extracting clinical pharmacology information, even with minimal training data.

Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computational Biology

Background:

  • Large language models (LLMs) offer advanced conversational capabilities but often require data transmission to external servers.
  • Online LLM use poses data privacy risks, especially for sensitive information.
  • Organizations prioritizing data protection, like regulatory agencies, need secure, local AI solutions.

Purpose of the Study:

  • To evaluate the feasibility of implementing open-source LLMs within a secure local network.
  • To assess LLM performance in extracting clinical pharmacology information from drug labels.
  • To determine the efficacy of LLMs for sensitive data processing in regulated environments.

Main Methods:

  • Implementation of various open-source LLMs within a regulatory agency's local network.
Keywords:
FDA labelsfew-shot learninglarge language modelspharmacokinetic drug–drug interactions and intrinsic factors

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  • Performance assessment using few-shot and zero-shot learning on specific NLP tasks.
  • Evaluation of a selected LLM for identifying drug exposure factors without fine-tuning.

    • Main Results:

    • Some open-source LLMs achieved performance comparable or superior to traditional models with minimal training.
    • A selected LLM accurately identified factors affecting drug exposure with 78.5% accuracy on a large dataset.
    • The study demonstrated successful local deployment for sensitive data analysis.

    Conclusions:

    • Open-source LLMs can be effectively implemented in secure local networks for sensitive data tasks.
    • LLMs offer a viable solution for natural language processing when extensive training data is unavailable.
    • This approach enhances data privacy and security for regulatory and other high-priority organizations.
    prompt engineering
    zero-shot learning