AI Research
RL-ATTACK: A Generalized Black-Box Adversarial Attack on Language Models via Reinforcement Learning
A reinforcement-learning approach targets language models through black-box adversarial attacks
Large language models (LLMs) exhibit vulnerabilities to adversarial perturbations that can induce unintended responses.
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ArXiv CS.CL
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ArXiv CS.CL
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The work highlights how adversarial perturbations can prompt language models to give unintended responses. A black-box attack using reinforcement learning underscores the importance of evaluating model robustness.
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