Adversarial Defence without Adversarial Defence: Enhancing Language Model Robustness via Instance-level Principal Component Removal

2025-12-25 · Source: Transactions of the Association for Computational Linguistics · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Cybersecurity & Data Privacy · Depth: Advanced, quick

Summary

A new add-on module enhances the adversarial robustness of pre-trained language models (PLMs) without requiring computationally expensive adversarial training. Developed by researchers from the Universities of Sheffield, Durham, Manchester, and Southampton, this method utilizes a variant of Principal Component Analysis (PCA) to transform the embedding space of PLMs. The transformation approximates Gaussian properties, which reduces the susceptibility of the models to adversarial perturbations while preserving essential semantic relationships. This technique minimizes the impact of adversarial noise on decision boundaries, thereby improving robustness. Evaluations across eight benchmark datasets demonstrate that the approach maintains comparable accuracy before attacks while significantly boosting adversarial robustness, achieving a balanced trade-off between resilience and generalization.

Key takeaway

For research scientists developing or deploying PLMs, this work offers a novel, computationally efficient method to improve model robustness. You can enhance your models' resilience to adversarial attacks by integrating this PCA-based add-on module, potentially avoiding the high costs associated with traditional adversarial training. Consider evaluating this approach on your specific PLM applications to balance robustness and generalization effectively.

Key insights

Transforming PLM embedding spaces to approximate Gaussian properties enhances adversarial robustness without costly adversarial training.

Principles

• Gaussian-like embeddings resist adversarial noise.
• Preserve semantic relationships during transformation.

Method

An add-on module uses a PCA variant to transform PLM embedding spaces, approximating Gaussian properties to reduce adversarial perturbation susceptibility and minimize noise impact on decision boundaries.

In practice

• Apply PCA-based module to existing PLMs.
• Evaluate robustness on benchmark datasets.

Topics

• Language Model Robustness
• Adversarial Attacks
• Principal Component Analysis
• Natural Language Processing
• Embedding Space Transformation

Best for: Research Scientist, AI Researcher, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Transactions of the Association for Computational Linguistics.