Summary of More Agents Is All You Need, by Junyou Li et al.
More Agents Is All You Need
by Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu, Deheng Ye
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 scaling of large language models (LLMs) by introducing a novel method called Agent Forest. The approach relies on a sampling-and-voting mechanism, which demonstrates that performance grows with the number of instantiated agents. This finding is particularly notable as it is orthogonal to existing methods aimed at enhancing LLMs. Furthermore, the degree of enhancement is found to be correlated with task difficulty. To verify this discovery and explore its properties, the authors conduct a wide range of experiments on various LLM benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that by using more agents, language models get better! The researchers created a new way called Agent Forest that helps language models work better just by having more “voters” or “agents” helping to make decisions. This is cool because it’s not like other ways people tried to improve language models before. They also found out that how well it works depends on the task, like if it’s an easy or hard question. The scientists tested this idea many times and showed that it really does work. |
