MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown exceptional results when working individually, and have reduced parameter size and inference times. |
| Approach: | They evaluate the behavior of a network of models collaborating through debate under the influence of an adversary and examine inference-time methods to generate more compelling arguments. |
| Outcome: | The proposed model-based model-driven analysis shows that the model-led model-mediated debates generate more compelling arguments and provide a defensive strategy. |
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