Summary of Rethinking the Bounds Of Llm Reasoning: Are Multi-agent Discussions the Key?, by Qineng Wang et al.
Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?
by Qineng Wang, Zihao Wang, Ying Su, Hanghang Tong, Yangqiu Song
First submitted to arxiv on: 28 Feb 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 A recent surge in Large Language Model (LLM) discussions has led to claims that multi-agent discussions can significantly enhance their reasoning capabilities. This paper reexamines this claim through systematic experiments, introducing a novel group discussion framework that expands the set of discussion mechanisms. The results show that a single-agent LLM with strong prompts can achieve comparable performance to the best existing discussion approach on various reasoning tasks and backbone LLMs. Interestingly, only when there is no demonstration in the prompt does multi-agent discussion outperform a single agent. Our study also reveals common interaction mechanisms between LLMs during discussions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are getting smarter! Researchers have been talking about how LLMs can learn from each other in groups, making them even better at solving problems. This paper looks at that idea and does some experiments to see if it really works. They came up with a new way for the models to talk to each other and found out that sometimes having just one super-smart model is almost as good as having multiple models chat together. The only time group discussion makes a big difference is when there’s no clear answer to follow, and even then, it’s not always better. |
Keywords
» Artificial intelligence » Large language model » Prompt
