More Agents Is All You need

This paper introduces a method that significantly enhances Large Language Model (LLM) performance by scaling up the number of agents using a sampling-and-voting approach. It shows a direct correlation between agent count and task performance improvement, making LLMs more efficient in complex problem-solving without needing complex frameworks.

Problem
LLMs face challenges in processing complex tasks due to limitations in existing enhancement methods like Chain of Thought (CoT) pipelines and multi-agent collaboration frameworks. These methods are intricate and not always effective across different tasks, leading to inaccuracies and inefficiencies in model outputs.

Solution
The researchers tackled this issue by implementing a straightforward method of adding more instantiated agents and applying a sampling-and-voting technique. This approach is complementary to existing methods, allowing for combined use for further performance gains. The study also explores task difficulty dimensions to optimize agent scaling effectively.

Results
The findings reveal that increasing the ensemble of agents consistently improves LLM performance, especially in tasks with varying difficulty levels. The performance gains are more pronounced with higher task complexity and longer reasoning steps, indicating a scalable and cost-effective way to boost LLM capabilities without the need for complex enhancements.