用多智能体辩论对抗对抗性攻击
Combating Adversarial Attacks with Multi-Agent Debate
AI总结：
本文研究利用多智能体辩论降低语言模型在红队对抗性攻击下的毒性，并通过嵌入聚类分析不同模型对不同攻击主题的易感性。
摘要：
尽管最先进的语言模型已取得令人瞩目的成果，但它们仍易受推理时对抗性攻击的影响，例如红队生成的对抗性提示（arXiv:2209.07858）。为提升语言模型生成质量的总体水平，一种被提出的方法是多智能体辩论，即语言模型通过讨论与反馈进行自我评估（arXiv:2305.14325）。我们在当前最先进的语言模型之间实现了多智能体辩论，并在单智能体和多智能体两种设置下评估模型对红队攻击的易感性。我们发现，当被越狱或能力较弱的模型被迫与未被越狱或能力更强的模型辩论时，多智能体辩论可以降低模型毒性。我们还发现，通过普遍使用多智能体交互可以获得边际改进。此外，我们通过嵌入聚类进行对抗性提示内容分类，并分析了不同模型对不同类型攻击主题的易感性。
英文摘要：
While state-of-the-art language models have achieved impressive results, they remain susceptible to inference-time adversarial attacks, such as adversarial prompts generated by red teams arXiv:2209.07858. One approach proposed to improve the general quality of language model generations is multi-agent debate, where language models self-evaluate through discussion and feedback arXiv:2305.14325. We implement multi-agent debate between current state-of-the-art language models and evaluate models' susceptibility to red team attacks in both single- and multi-agent settings. We find that multi-agent debate can reduce model toxicity when jailbroken or less capable models are forced to debate with non-jailbroken or more capable models. We also find marginal improvements through the general usage of multi-agent interactions. We further perform adversarial prompt content classification via embedding clustering, and analyze the susceptibility of different models to different types of attack topics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
