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arXiv · 2405.17361

A One-Layer Decoder-Only Transformer is a Two-Layer RNN: With an Application to Certified Robustness

Yuhao Zhang·Aws Albarghouthi·Loris D'Antoni
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Abstract

This paper reveals a key insight that a one-layer decoder-only Transformer is equivalent to a two-layer Recurrent Neural Network (RNN). Building on this insight, we propose ARC-Tran, a novel approach for verifying the robustness of decoder-only Transformers against arbitrary perturbation spaces. Compared to ARC-Tran, current robustness verification techniques are limited either to specific and length-preserving perturbations like word substitutions or to recursive models like LSTMs. ARC-Tran addresses these limitations by meticulously managing position encoding to prevent mismatches and by utilizing our key insight to achieve precise and scalable verification. Our evaluation shows that ARC-Tran (1) trains models more robust to arbitrary perturbation spaces than those produced by existing techniques and (2) shows high certification accuracy of the resulting models.

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Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni. 2024-05-27. A One-Layer Decoder-Only Transformer is a Two-Layer RNN: With an Application to Certified Robustness. https://arxiv.org/abs/2405.17361

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