In Transformer We Trust? A Perspective on Transformer Architecture Failure Modes

2026-02-15

AI Summary

This study addresses the trustworthiness challenges confronting Transformer models in high-stakes applications, including issues of interpretability, robustness, fairness, and privacy. It presents the first cross-domain systematic analysis by integrating interpretability techniques, adversarial robustness evaluations, fairness assessments, and privacy audits to comprehensively examine failure modes across natural language processing, computer vision, and scientific engineering domains—such as healthcare, climate modeling, and nuclear science. The investigation uncovers both shared architectural vulnerabilities and domain-specific risks inherent to Transformer-based systems. These findings provide a theoretical foundation for the reliable deployment of Transformers in safety-critical settings and delineate promising new directions for research in trustworthy artificial intelligence.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Transformer architectures have revolutionized machine learning across a wide range of domains, from natural language processing to scientific computing. However, their growing deployment in high-stakes applications, such as computer vision, natural language processing, healthcare, autonomous systems, and critical areas of scientific computing including climate modeling, materials discovery, drug discovery, nuclear science, and robotics, necessitates a deeper and more rigorous understanding of their trustworthiness. In this work, we critically examine the foundational question: \textitHow trustworthy are transformer models?} We evaluate their reliability through a comprehensive review of interpretability, explainability, robustness against adversarial attacks, fairness, and privacy. We systematically examine the trustworthiness of transformer-based models in safety-critical applications spanning natural language processing, computer vision, and science and engineering domains, including robotics, medicine, earth sciences, materials science, fluid dynamics, nuclear science, and automated theorem proving; highlighting high-impact areas where these architectures are central and analyzing the risks associated with their deployment. By synthesizing insights across these diverse areas, we identify recurring structural vulnerabilities, domain-specific risks, and open research challenges that limit the reliable deployment of transformers.

Problem

• trustworthiness
• Transformer architecture
• failure modes
• reliability
• safety-critical applications

Innovation

• Transformer trustworthiness
• failure modes
• adversarial robustness
• interpretability
• safety-critical applications