Unitary Multi-Margin BERT for Robust Natural Language Processing (2410.12759v1)

Abstract: Recent developments in adversarial attacks on deep learning leave many mission-critical NLP systems at risk of exploitation. To address the lack of computationally efficient adversarial defense methods, this paper reports a novel, universal technique that drastically improves the robustness of Bidirectional Encoder Representations from Transformers (BERT) by combining the unitary weights with the multi-margin loss. We discover that the marriage of these two simple ideas amplifies the protection against malicious interference. Our model, the unitary multi-margin BERT (UniBERT), boosts post-attack classification accuracies significantly by 5.3% to 73.8% while maintaining competitive pre-attack accuracies. Furthermore, the pre-attack and post-attack accuracy tradeoff can be adjusted via a single scalar parameter to best fit the design requirements for the target applications.

Unitary Multi-Margin BERT: Enhancing Robustness in NLP against Adversarial Attacks

The paper by Hao-Yuan Chang and Kang L. Wang introduces an innovative framework named Unitary Multi-Margin BERT (UniBERT) aimed at boosting the robustness of Bidirectional Encoder Representations from Transformers (BERT) against adversarial attacks. The work primarily addresses a critical vulnerability in deep learning-based NLP systems, which are prone to adversarial interventions. This paper makes significant contributions by incorporating both multi-margin loss and unitary weights to enhance the robustness of NLP models.

Core Innovations

The research introduces two main methodological innovations:

1. Multi-Margin Loss: Unlike traditional cross-entropy loss, the multi-margin loss encourages a larger margin of safety between the model's logits and the decision boundaries during the finetuning process. This modification results in more distinctive neural representations, effectively increasing the input perturbation threshold needed to cause misclassification. The theoretical basis provided builds on the assumption that the Mahalanobis distance between classes' neural representations is maximized, thus improving adversarial robustness significantly.
2. Unitary Weights: By constraining certain weight matrices in BERT to be unitary, the proposed model maintains the perturbation magnitude injected by adversaries within bounds, preventing amplification through successive layers of the network. This property ensures stability by preserving the cosine distance between original and perturbed sentence embeddings, reducing the likelihood of adversarial manipulation altering classification outcomes.

Experimental Evaluation

The authors conduct comprehensive experiments demonstrating that UniBERT significantly outperforms baseline models (BERT, RoBERTa, ALBERT, DistilBERT) and state-of-the-art defense strategies (AMDA, MRAT, InfoBERT) in post-attack accuracy across three NLP tasks: text categorization, natural language inference, and sentiment analysis. UniBERT achieved improvements in post-attack accuracy by at least 5.3% to a remarkable 73.8% compared to existing defense methodologies, without substantial detriment to pre-attack accuracy.

Methodological Insights

The paper presents a detailed ablation paper showing that combining multi-margin loss with unitary weights is essential for achieving optimal robustness. Alone, unitarity does not yield significant improvements under severe adversarial conditions. Similarly, multi-margin loss enhances robustness but is less effective without the stability provided by unitary weights.

Additionally, the authors highlight how UniBERT's attention mechanism stabilizes perturbations through unitary constraints sequentially applied across 12 attention layers. This stabilization contributes to higher consistency in adversarial robustness across diverse attack scenarios, exemplified by the high and steady cosine similarity between activations of original and perturbed inputs.

Theoretical Implications and Future Directions

Unitary Multi-Margin BERT has several theoretical implications, particularly in understanding how distinct neural representations and stability can be harmonized to secure NLP models against adversarial attacks synergistically. The results suggest that future work could explore further applications of unitary transformations in neural architectures, potentially extending beyond NLP to other domains susceptible to adversarial threats.

Moreover, the paper opens a path for integrating these techniques into other transformer architectures, which might be beneficial for tasks requiring stringent robustness guarantees. Further exploration could focus on optimizing unitarity constraints and tuning multi-margin parameters for specific applications, broadening the utility and applicability of this innovative approach in deep learning.