Tencent researchers recently published a paper revealing new ways to improve the performance of large language models. The study found that by integrating multiple small language models (LLMs), the overall model performance can be significantly improved without the need for complex collaboration frameworks, even surpassing a single large LLM. The paper elaborates on this finding and proposes two optimization strategies: stepwise sampling and voting, and stratified sampling and voting, to further improve model efficiency and accuracy. This research provides new ideas for the development of large language models and points out the direction for future model construction and optimization.
Tencent researchers have found that the performance of large language models will increase as the number of instantiated agents increases, without the need for a complex multi-LLM agents collaboration framework. Experimental results show that ensembles of multiple small LMs can surpass the performance of larger LMs. The paper explores the relationship between performance improvement and problem difficulty, and proposes two optimization strategies: gradual sampling and voting, and stratified sampling and voting.
The research results are of great significance and provide new directions and ideas for the optimization of large language models. In the future, through further research and improvement of these two optimization strategies, the performance of large language models can be better improved and applied in a wider range of fields. This will promote the development of artificial intelligence technology and bring more possibilities to all walks of life.