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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 12·5 min read
LLMs as Post-hoc Auditors of Physiological Plausibility in Symbolic Regression: A Clinician-Evaluated Case Study
TL;DR
Large Language Models (LLMs) were evaluated as tools to enhance the interpretability of mathematical expressions derived from data in Symbolic Regression. The study found that while LLMs improved comparative assessments of these expressions, they also produced some scientifically questionable interpretations.
✦ Why It Matters
Consider using LLMs to assist in evaluating the interpretability of your Symbolic Regression models.
Key Takeaways
- Utilize LLMs for comparative analysis of mathematical models.
- Engage clinicians to validate LLM outputs for medical applications.
- Be cautious of LLM-generated interpretations; they may lack scientific accuracy.
- Explore LLMs as post-processing tools rather than standalone validators.
How It Works
LLMs analyze and rank mathematical expressions generated by Genetic Programming based on their interpretability and medical plausibility. By comparing outputs across multiple runs, LLMs can highlight which expressions are more favorable from a clinical perspective.
⚠ The Catch
LLMs produced some interpretations that were physiologically and mathematically questionable, indicating their limitations in providing autonomous validation.