Summary of Efficient Adversarial Training in Llms with Continuous Attacks, by Sophie Xhonneux et al.
Efficient Adversarial Training in LLMs with Continuous Attacks
by Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel, Leo Schwinn
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to improve the robustness of large language models (LLMs) against adversarial attacks is proposed in this paper. The authors identify that current methods for adversarial training are computationally expensive due to the need to perform discrete attacks at each iteration. To address this, they introduce a fast adversarial training algorithm called C-AdvUL, which calculates adversarial attacks in the continuous embedding space of the LLM, resulting in significant efficiency gains. The proposed algorithm consists of two losses: one that makes the model robust against continuous attacks and another that fine-tunes the model on utility data to ensure its usefulness. Additionally, the authors introduce an adversarial variant of IPO called C-AdvIPO that does not require utility data for adversarially robust alignment. Experimental results on five LLM models from different families and scales demonstrate that both algorithms substantially enhance robustness against discrete attacks while maintaining utility. The findings suggest that robustness to continuous perturbations can extrapolate to discrete threat models, paving the way for scalable adversarial training algorithms for robustly aligning LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are vulnerable to fake inputs that can trick them into giving incorrect answers. To make these models more secure, researchers have developed a method called adversarial training. However, this method is very slow and expensive because it requires many calculations at each step. A new way of doing adversarial training has been discovered that is much faster and uses less computer power. This method calculates fake inputs in the same space where the model makes its predictions. The authors of this paper propose two new algorithms for fast adversarial training, one called C-AdvUL and another called C-AdvIPO. They test these algorithms on five different models and find that they make the models much more secure against fake inputs while still giving good answers. This is important because it means we can use these algorithms to improve the security of large language models. |
Keywords
» Artificial intelligence » Alignment » Embedding space
