title: More Agents Is All You Need
summary: We find that, simply via a sampling-and-voting method, the performance of
  large language models (LLMs) scales with the number of agents instantiated.
  Also, this method is orthogonal to existing complicated methods to further
  enhance LLMs, while the degree of enhancement is correlated to the task
  difficulty. We conduct comprehensive experiments on a wide range of LLM
  benchmarks to verify the presence of our finding, and to study the properties
  that can facilitate its occurrence. Our code is publicly available at:
  https://anonymous.4open.science/r/more_agent_is_all_you_need.
paper_authors._id: 65c592e52b319ae036ffedf9
paper_authors.name: Junyou Li
paper_authors._id: 65c592e52b319ae036ffedfa
paper_authors.name: Qin Zhang
paper_authors._id: 65c592e52b319ae036ffedfb
paper_authors.name: Yangbin Yu
paper_authors._id: 65c592e52b319ae036ffedfc
paper_authors.name: Qiang Fu
paper_authors._id: 65c592e52b319ae036ffedfd
paper_authors.name: Deheng Ye
paper_title: More Agents Is All You Need
paper_summary: We find that, simply via a sampling-and-voting method, the performance of
  large language models (LLMs) scales with the number of agents instantiated.
  Also, this method is orthogonal to existing complicated methods to further
  enhance LLMs, while the degree of enhancement is correlated to the task
  difficulty. We conduct comprehensive experiments on a wide range of LLM
  benchmarks to verify the presence of our finding, and to study the properties
  that can facilitate its occurrence. Our code is publicly available at:
  https://anonymous.4open.science/r/more_agent_is_all_you_need.
paper_ai_keywords: sampling-and-voting method large language models (LLMs) agents existing complicated methods parameter enhancement task difficulty benchmarks public health