Combating Adversarial Attacks with Multi-Agent Debate
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916088591155200 |
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| author | Chern, Steffi Fan, Zhen Liu, Andy |
| author_facet | Chern, Steffi Fan, Zhen Liu, Andy |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_05998 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Combating Adversarial Attacks with Multi-Agent Debate Chern, Steffi Fan, Zhen Liu, Andy Computation and Language Artificial Intelligence 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. |
| title | Combating Adversarial Attacks with Multi-Agent Debate |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2401.05998 |