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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Related Experiment Video

Updated: Sep 12, 2025

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Developing foundations for biomedical knowledgebases from literature using large language models - A systematic

Chen Miao1, Zhenghao Zhang2, Jiamin Chen2

  • 1School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.

Computational and Structural Biotechnology Journal
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

Evaluating large language models (LLMs) for biomedical knowledge extraction is crucial. Our benchmark reveals significant performance variations, highlighting the need for careful prompt engineering and source verification.

Keywords:
Biomedical knowledgebasesLarge language modelsPrompt engineering

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Large language models (LLMs) show potential in biomedical applications.
  • Reliable knowledge extraction from biomedical literature is essential for knowledge base development.
  • Current methods for evaluating LLM reliability in this domain are insufficient.

Purpose of the Study:

  • To develop and utilize a benchmark for comparing LLM performance in biomedical knowledge extraction.
  • To assess LLM capabilities across 11 diverse knowledge extraction tasks.
  • To investigate the impact of task-specific examples on LLM performance.

Main Methods:

  • Creation of a benchmark suite for 11 literature knowledge extraction tasks.
  • Comparison of multiple LLMs on these tasks, with and without in-context examples.
  • Analysis of LLM performance based on task complexity, data characteristics, and output requirements.

Main Results:

  • Significant performance variability observed across different LLMs and tasks.
  • LLM performance is influenced by technical specialization, task difficulty, information scattering, and standardization needs.
  • Requiring LLMs to cite source text improves reliability but presents prompt engineering challenges.

Conclusions:

  • LLM performance in biomedical knowledge extraction is highly variable and task-dependent.
  • Careful prompt design, including source text citation, is necessary for reliable LLM application.
  • Further research is needed to optimize LLM prompting for robust biomedical knowledge extraction.