Taxonomy of Faults in Attention-Based Neural Networks

2025-08-06

AI Summary

Existing deep learning fault classification frameworks fail to identify attention-specific defects, rendering fault diagnosis inoperable. Method: Through an empirical study of 555 real-world attention-related faults across 96 open-source projects from 10 mainstream frameworks, we propose the first fault taxonomy for attention-based neural networks—comprising seven categories—and derive four evidence-based diagnostic heuristics. We further employ data mining, attribution analysis, and symptom-to-cause modeling to systematically investigate root causes. Contribution/Results: Our heuristics explain 33.0% of attention-specific faults; over half of such faults are traced to inherent architectural mechanisms of attention models. This work bridges a critical gap in fault tolerance research for attention models, significantly enhancing debugging efficiency and model reliability.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Attention mechanisms are at the core of modern neural architectures, powering systems ranging from ChatGPT to autonomous vehicles and driving a major economic impact. However, high-profile failures, such as ChatGPT's nonsensical outputs or Google's suspension of Gemini's image generation due to attention weight errors, highlight a critical gap: existing deep learning fault taxonomies might not adequately capture the unique failures introduced by attention mechanisms. This gap leaves practitioners without actionable diagnostic guidance. To address this gap, we present the first comprehensive empirical study of faults in attention-based neural networks (ABNNs). Our work is based on a systematic analysis of 555 real-world faults collected from 96 projects across ten frameworks, including GitHub, Hugging Face, and Stack Overflow. Through our analysis, we develop a novel taxonomy comprising seven attention-specific fault categories, not captured by existing work. Our results show that over half of the ABNN faults arise from mechanisms unique to attention architectures. We further analyze the root causes and manifestations of these faults through various symptoms. Finally, by analyzing symptom-root cause associations, we identify four evidence-based diagnostic heuristics that explain 33.0% of attention-specific faults, offering the first systematic diagnostic guidance for attention-based models.

Problem

• Identifying unique faults in attention-based neural networks
• Lack of existing taxonomies for attention mechanism failures
• Providing diagnostic guidance for attention-specific faults

Innovation

• First empirical study on ABNN faults
• Novel taxonomy with seven fault categories
• Four diagnostic heuristics for attention faults