Summary of Acc-collab: An Actor-critic Approach to Multi-agent Llm Collaboration, by Andrew Estornell et al.
ACC-Collab: An Actor-Critic Approach to Multi-Agent LLM Collaboration
by Andrew Estornell, Jean-Francois Ton, Yuanshun Yao, Yang Liu
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces ACC-Collab, an Actor-Critic based learning framework for improving the efficacy of large language models (LLMs) through iterative dialog between multiple models. The authors propose a two-agent team consisting of an actor-agent and a critic-agent that learns collaboration as a learned behavior rather than an emergent one. This approach outperforms state-of-the-art (SotA) multi-agent techniques on a wide range of benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ACC-Collab is a new way to make language models work better together. Right now, these models are mostly used for talking with humans, but they can also help each other learn and get better at tasks like answering questions or translating text. The problem is that these models don’t really know how to work together well yet. This paper shows a new way to teach them how to collaborate, using a special kind of learning framework called ACC-Collab. It makes two kinds of agents: one that does the actual work and another that helps it learn from its mistakes. The results show that this approach works really well and is better than what other researchers have tried so far. |
