Summary of Persona Inconstancy in Multi-agent Llm Collaboration: Conformity, Confabulation, and Impersonation, by Razan Baltaji et al.
Persona Inconstancy in Multi-Agent LLM Collaboration: Conformity, Confabulation, and Impersonation
by Razan Baltaji, Babak Hemmatian, Lav R. Varshney
First submitted to arxiv on: 6 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 This paper investigates the performance of Large Language Model (LLM) agents in simulating collective decision-making processes through cross-national collaboration and debate. The authors examine AI agent ensembles engaged in discussions, analyzing private responses and chat transcripts to determine their reliability in adopting assigned personas and mimicking human interactions. The study finds that multi-agent discussions can support more diverse perspectives, but this effect is limited by the agents’ susceptibility to conformity due to peer pressure and challenges in maintaining consistent opinions. The authors also identify instructions encouraging debate rather than collaboration as a factor contributing to increased inconstancy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how AI systems with multiple “agents” work together to make decisions. It’s like a big group project where each agent has its own thoughts and opinions. The researchers studied these agents talking to each other and found that they can come up with more diverse ideas, but sometimes they follow what the others think rather than sticking to their own views. They also discovered that when agents are asked to argue instead of work together, they become even less consistent in their opinions. |
Keywords
» Artificial intelligence » Large language model
