English(EN) LLMs as Post-hoc Auditors of Physiological Plausibility in Symbolic Regression: A Clinician-Evaluated Case Study

大型语言模型在符号回归模型的事后审计方面显示出潜力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-12 04:00

研究人员探索了使用大型语言模型（LLMs）来审计符号回归模型在生理学上的合理性，特别是在医学背景下。虽然LLMs在对演化出的数学表达式进行排名方面显示出潜力，但它们的解释有时在生理学和数学上存在疑问。临床医生发现LLMs的比较排名比孤立的术语解释更有用，这表明LLMs更适合专家监督的审计，而不是自主验证。 AI

影响大型语言模型可以协助验证复杂的科学模型，有可能加速医学等领域的发现。

排序理由该集群包含一篇详细介绍新研究方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jorge L\'opez-Varela, J. Ignacio Hidalgo, Jos\'e-Manuel Mu\~noz, Omar Costilla-Reyes, Esther Maqueda, Jesus Moreno-Fernandez, Tom\'as Gonz\'alez-Vidal, J. Manuel Velasco, Oscar Garnica · 2026-09-12 04:00

大型语言模型作为符号回归中生理学合理性的事后审计者：一项临床医生评估的案例研究

arXiv:2609.11431v1 Announce Type: new Abstract: Genetic Programming and its variants, such as grammatical evolution, are widely used in Symbolic Regression to derive mathematical expressions from multivariate data. In addition to predictive accuracy, models are appreciated for th…

本文如何被排名

报道来源 [1]

大型语言模型作为符号回归中生理学合理性的事后审计者：一项临床医生评估的案例研究

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