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We propose the use of Large Language Models (LLMs) for generating statistically supported hypotheses from scientific literature. We present a two-stage framework that effectively leverages LLMs’ capacity to analyze vast literature and extract pertinent information to formulate evidence-based hypotheses. Our method comprises two phases: 1) data extraction via decomposed zero-shot prompting, and 2) hypothesis generation by auto-formulating and solving an optimization problem. We demonstrate this framework in agricultural science, where field data is particularly limited.

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LLMs Tackle Meta-analysis: Automating Scientific Hypothesis Generation with Statistical Rigor

  • 会议论文

  • 发表日期：

    2025年

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  • Tung-Wei Lin,
  • Runing Yang,
  • Zain ul Abdeen,
  • Alberto Sangiovanni-Vincentelli,
  • Haibo Huang,
  • Ming Jin

摘要

We propose the use of Large Language Models (LLMs) for generating statistically supported hypotheses from scientific literature. We present a two-stage framework that effectively leverages LLMs’ capacity to analyze vast literature and extract pertinent information to formulate evidence-based hypotheses. Our method comprises two phases: 1) data extraction via decomposed zero-shot prompting, and 2) hypothesis generation by auto-formulating and solving an optimization problem. We demonstrate this framework in agricultural science, where field data is particularly limited.