LLMs can reveal clinical associations via comparison questions, aiding medical decision-making.

Researchers have developed a novel method to extract associations between clinical variables from large language models (LLMs) using structured comparison questions. This approach, demonstrated in domains like COPD and multiple sclerosis, employs patient comparison triplet questions and a statistical model to estimate correlations without direct access to the LLM's internal workings. The method aims to provide a cautious pathway from implicit correlations within LLM training data to potential causal statements, supporting medical decision-making. AI

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IMPACT Introduces a new method for inferring clinical associations from LLMs, potentially aiding medical research and decision-making.

RANK_REASON This is a research paper published on arXiv detailing a new method for extracting clinical associations from LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

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• LLM
• COPD
• multiple sclerosis
• arXiv

• paper
• safety