研究人员开发了SCOPE，一个新颖的多LLM框架，旨在提高在临床试验数据上的推理能力。该方法通过在生成答案之前明确构建规划过程，解决了当前LLM中“糟糕的推理”问题。SCOPE将任务分解为行选择、结构化规划和执行，使得源字段、规则和约束更加清晰。评估表明，SCOPE提高了复杂推理问题的准确性，并与其他方法相比提供了更好的准确性-效率权衡。 AI
影响 引入了一个结构化规划框架，以提高LLM在复杂临床试验数据推理任务上的准确性。
排序理由 这是一篇研究论文，详细介绍了一个用于基于LLM的临床试验数据推理的新框架。
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arXiv:2604.25120v1 Announce Type: new Abstract: We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through normalization, classification, extraction, or lightweight domain reasoning. Mot…
We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through normalization, classification, extraction, or lightweight domain reasoning. Motivated by the observation that current LLM appro…
We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through normalization, classification, extraction, or lightweight domain reasoning. Motivated by the observation that current LLM appro…