Closing the Distribution Gap in Adversarial Training for LLMs
arXiv:2602.15238v1 Announce Type: new Abstract: Adversarial training for LLMs is one of the most promising methods to reliably improve robustness against adversaries. However, despite significant progress, models remain vulnerable to simple in-distribution exploits, such as rewriting prompts in the past tense or translating them into other languages. We argue that this persistent fragility stems from a fundamental limitation in current adversarial training algorithms: they minimize adversarial loss on their training set but inadequately cover the data distribution, resulting in vulnerability to seemingly simple attacks. To bridge this gap, we propose Distributional Adversarial Training, DAT. We leverage Diffusion LLMs to approximate the true joint distribution of prompts and responses, enabling generation of diverse, high-likelihood samples that address generalization failures. By combining optimization over the data distribution provided by the diffusion model with continuous adversa
arXiv:2602.15238v1 Announce Type: new Abstract: Adversarial training for LLMs is one of the most promising methods to reliably improve robustness against adversaries. However, despite significant progress, models remain vulnerable to simple in-distribution exploits, such as rewriting prompts in the past tense or translating them into other languages. We argue that this persistent fragility stems from a fundamental limitation in current adversarial training algorithms: they minimize adversarial loss on their training set but inadequately cover the data distribution, resulting in vulnerability to seemingly simple attacks. To bridge this gap, we propose Distributional Adversarial Training, DAT. We leverage Diffusion LLMs to approximate the true joint distribution of prompts and responses, enabling generation of diverse, high-likelihood samples that address generalization failures. By combining optimization over the data distribution provided by the diffusion model with continuous adversarial training, DAT achieves substantially higher adversarial robustness than previous methods.
Executive Summary
This article proposes a novel approach to adversarial training for Large Language Models (LLMs) called Distributional Adversarial Training (DAT). DAT aims to address the distribution gap in current adversarial training algorithms by leveraging Diffusion LLMs to approximate the true joint distribution of prompts and responses. This enables the generation of diverse, high-likelihood samples that alleviate generalization failures. The authors demonstrate that DAT achieves significantly higher adversarial robustness than previous methods. While the article presents a compelling case for DAT, its practical implementation and scalability remain to be explored. The proposed approach has the potential to bridge the gap between model performance on the training set and real-world environments, making it an exciting development in the field of LLMs.
Key Points
- ▸ Distributional Adversarial Training (DAT) proposes a novel approach to address the distribution gap in adversarial training for LLMs.
- ▸ DAT leverages Diffusion LLMs to approximate the true joint distribution of prompts and responses.
- ▸ The approach enables the generation of diverse, high-likelihood samples that alleviate generalization failures.
Merits
Strength in theoretical foundations
The article is grounded in a thorough understanding of the limitations of current adversarial training algorithms and proposes a well-motivated solution.
Demerits
Assumes access to high-quality training data
The proposed approach relies on the availability of a large, diverse dataset to train the Diffusion LLM, which may not be feasible for all applications.
Expert Commentary
The article presents a significant contribution to the field of adversarial training for LLMs. The proposed approach is well-motivated and theoretically sound, and the experimental results demonstrate its effectiveness. However, the article's focus on the distribution gap may overlook other important aspects of adversarial training, such as the role of data augmentation and regularization techniques. Additionally, the proposed approach assumes access to high-quality training data, which may not be feasible for all applications. Nevertheless, the article's findings have the potential to significantly impact the development of more robust and trustworthy LLMs.
Recommendations
- ✓ Future research should investigate the scalability and practical implementation of the proposed approach in various applications.
- ✓ The article's findings should be further explored in the context of other machine learning models and tasks to assess their generalizability.
