Toward Monosemantic Clinical Explanations for Alzheimer’s Diagnosis via Attribution and Mechanistic Interpretability

Michail Mamalakis, Tiansi Dong, Tiago Azevedo, Cristian Cosentino, Chiara D'Ercoli, Subati Abulikemu, Zhongtian Sun, Richard Bethlehem, Pietro Lio

Keywords: mechanistic-interpretability, attributional-interpretability, neurodegenerative-disease, LLM, sparse-autoencoders, explanation-optimizer

Abstract: Interpretability remains a major obstacle to deploying large language models (LLMs) in high-stakes settings such as Alzheimer’s disease (AD) progression diagnosis, where early and explainable predictions are essential. Traditional attribution methods suffer from inter-method variability and often produce unstable explanations due to the polysemantic nature of LLM representations, while mechanistic interpretability lacks direct alignment with model inputs/outputs and does not provide importance scores. We propose a unified interpretability framework that integrates attributional and mechanistic perspectives through monosemantic feature extraction. Our approach evaluates six attribution techniques, refines them using a learning-based explanation optimizer, and employs sparse autoencoders (SAEs) to map LLM activations into a disentangled latent space that supports clearer and more coherent attribution analysis. Comparing latent-space and native attributions, we observe substantial gains in robustness, consistency, and semantic clarity. Experiments on IID and OOD Alzheimer’s cohorts across binary and three-class tasks demonstrate that our framework yields more reliable, clinically aligned explanations and reveals meaningful diagnostic patterns. This work advances the safe and trustworthy use of LLMs in cognitive health and neurodegenerative disease assessment.

Primary Area: interpretability and explainable AI

Submission Number: 22121