July 24, 2025

Application of Sparse Autoencoders to Enhance Mechanistic Interpretability of Large Language Models in Medicine (Preprint)

Puntos clave

MAIN FINDING: Sparse autoencoders enhance understanding of large language models in medicine.
KEY EVIDENCE: Incorporating human-interpretable features increases the trust in LLMs for clinical use.
APPROACH: The work utilizes sparse autoencoders for extracting useful features from LLMs.
SIGNIFICANCE: Improved interpretability may facilitate the adoption of LLMs in clinical practices, optimizing treatment efficiency.

Resumen

UNSTRUCTURED Large language models (LLM) are positioned to transform the practice of medicine through their ability to determine clinical diagnoses, form treatment plans, and optimize medical workflows. Understanding the internal mechanism by which these models operate is necessary to legitimize LLMs in clinical practice. This field of research is called mechanistic interpretability. By extracting human-interpretable features from LLMs, sparse autoencoders (SAEs) represent a promising development in the field of digital medicine.

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