The researchers conducted extensive experiments on a variety of LLM benchmarks to confirm their findings and to investigate the characteristics that can encourage this occurrence. The code used in their research is publicly accessible, allowing others in the field to replicate and build upon their work.
Key takeaways:
- The performance of large language models (LLMs) can be improved simply through a sampling-and-voting method, and this improvement scales with the number of agents instantiated.
- This method is orthogonal to existing complicated methods to further enhance LLMs.
- The degree of enhancement is correlated to the task difficulty.
- Comprehensive experiments were conducted on a wide range of LLM benchmarks to verify these findings and study the properties that can facilitate its occurrence.