This AI Paper from the National University of Singapore Introduces a Defense Against Adversarial Attacks on LLMs Utilizing Self-Evaluation

Ensuring the safety of Large Language Models (LLMs) has become a pressing concern in the ocean of a huge number of existing LLMs serving multiple domains. Despite the implementation of training methods like Reinforcement Learning from Human Feedback (RLHF) and the development of inference-time guardrails, many adversarial attacks have demonstrated the ability to bypass these […]

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