Can Hessian-Based Insights Support Fault Diagnosis in Attention-based Models?
This work addresses fault diagnosis for developers of complex neural architectures, but it is incremental as it applies existing Hessian methods to a new context.
The study tackled the challenge of diagnosing faults in attention-based models by evaluating Hessian-based analysis, showing that Hessian-derived metrics can localize instability and pinpoint fault sources more effectively than gradients alone in experiments on three models.
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.