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arXiv AI By 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

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

Read the original on arXiv AI →

The paper investigates using Large Language Models (LLMs) as post-hoc auditors for symbolic regression models produced by genetic programming. By having three LLMs analyze and rank four evolved expressions across multiple runs, the study compares the LLM-generated rankings and interpretations with assessments from three clinicians. Results show that LLMs provide more favorable comparative rankings than isolated term-level interpretations, yet they also generate physiologically and mathematically questionable explanations, suggesting they are best used under expert oversight rather than for autonomous validation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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