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MixAT: Combining Continuous and Discrete Adversarial Training for LLMs
- MixAT is a novel method that combines discrete and continuous adversarial attacks during training for Large Language Models (LLMs).
- The MixAT approach aims to improve the robustness of LLMs by addressing vulnerabilities to various types of attacks.
- By introducing a combination of discrete and continuous attacks, MixAT demonstrates better defense against adversarial attacks while maintaining runtime efficiency.
- The research highlights MixAT's potential for enhancing the safety and reliability of LLMs and offers a promising tradeoff between robustness and computational cost.
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