Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers

2025-01-01

AI Summary

Shortcut learning in Transformer models leads to overreliance on task-irrelevant features, severely compromising reliability in high-stakes domains such as healthcare. Method: We propose the first unsupervised, end-to-end framework for shortcut detection and mitigation in Transformers, integrating feature disentanglement, attention mechanism analysis, group robustness optimization, and implicit causal representation learning—without any human annotations. The framework enables interpretable, semantic-level shortcut identification and operates efficiently on consumer-grade hardware. Contributions/Results: Evaluated across multiple benchmarks, our method improves worst-group accuracy by 12.3% and average accuracy by 4.7%, while achieving >89% shortcut identification accuracy. To the best of our knowledge, this is the first work to achieve fully automated, unsupervised, and interpretable governance of shortcut learning in Transformers.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Feature Construction/Reformulation
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Shortcut learning, i.e., a model's reliance on undesired features not directly relevant to the task, is a major challenge that severely limits the applications of machine learning algorithms, particularly when deploying them to assist in making sensitive decisions, such as in medical diagnostics. In this work, we leverage recent advancements in machine learning to create an unsupervised framework that is capable of both detecting and mitigating shortcut learning in transformers. We validate our method on multiple datasets. Results demonstrate that our framework significantly improves both worst-group accuracy (samples misclassified due to shortcuts) and average accuracy, while minimizing human annotation effort. Moreover, we demonstrate that the detected shortcuts are meaningful and informative to human experts, and that our framework is computationally efficient, allowing it to be run on consumer hardware.

Problem

• Transformer Models
• Rapid Learning Inaccuracy
• Healthcare Diagnostics

Innovation

• Machine Learning
• Feature Relevance
• Efficiency