Summary of Multiagent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations Via Debate, by Alfonso Amayuelas et al.
MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate
by Alfonso Amayuelas, Xianjun Yang, Antonis Antoniades, Wenyue Hua, Liangming Pan, William Wang
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper explores the collaborative capabilities of Large Language Models (LLMs) when working together as agents to execute complex tasks. Building on their individual exceptional results, LLMs can be designed to interact with each other, leveraging specialized models for coding, and improving confidence through multiple computations. The study evaluates a network of models collaborating through debate under the influence of an adversary, introducing metrics to assess the adversary’s effectiveness in influencing system accuracy and model agreement. Key findings highlight the importance of a model’s persuasive ability in shaping others’ opinions. Furthermore, the research investigates inference-time methods for generating more compelling arguments and explores prompt-based mitigation as a defensive strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine having many super smart computers working together to solve really hard problems! That’s what this paper is about – how these “Large Language Models” can collaborate to get things done. Right now, they’re great at doing one thing well, but when they work together, they can do even more amazing things. The researchers looked at how these models behave when they disagree and try to convince each other of their answers. They found that the best models are good at persuading others to agree with them! They also explored ways to make these models come up with even better arguments. |
Keywords
» Artificial intelligence » Inference » Prompt
