Can Hessian-Based Insights Support Fault Diagnosis in Attention-based Models?

TL;DR

This paper investigates the use of Hessian-based analysis to improve fault diagnosis in attention-based deep learning models, demonstrating its effectiveness in identifying unstable regions and fault sources.

Contribution

It introduces Hessian-derived metrics for fault diagnosis in attention models and empirically evaluates their effectiveness across multiple architectures.

Findings

Hessian metrics outperform gradients in fault localization

Hessian analysis identifies fragile regions effectively

Applicable to diverse attention-based models

Abstract

As attention-based deep learning models scale in size and complexity, diagnosing their faults becomes increasingly challenging. In this work, we conduct an empirical study to evaluate the potential of Hessian-based analysis for diagnosing faults in attention-based models. Specifically, we use Hessian-derived insights to identify fragile regions (via curvature analysis) and parameter interdependencies (via parameter interaction analysis) within attention mechanisms. Through experiments on three diverse models (HAN, 3D-CNN, DistilBERT), we show that Hessian-based metrics can localize instability and pinpoint fault sources more effectively than gradients alone. Our empirical findings suggest that these metrics could significantly improve fault diagnosis in complex neural architectures, potentially improving software debugging practices.