Most Effective Team Structure For Small Language Models

Artificial intelligence researchers knew that multiple language models can give better results than a single model. But how much these teams can grow, which was uncertain if the structure is most efficient. A new research offers a wide experimental study to solve these questions.

The researchers tested eight different agent orientation architecture:

• Five pcs 7-9B parameters were evaluated on the instruction-based small language model
• Six different types of tasks including five short-cevap comparison and a executable code comparison
• Agent calls were tested by increasing up to 30 steps
• 17 points of accuracy healing in some tasks
• Performance gain showed three to thirty changes according to the type of task

Results reveal that the return of team scale is dependent on the task. While some problems are resolved with small teams, others benefit from more agents and more collaboration. This finding is a critical result for applications where resources are limited: rather than always prefer larger teams, it shows that the choice of architecture suitable for the nature of the task can be more rational.

The study is particularly important for limited budget organizations: more expensive, instead of large models, it seems to be able to create effective solutions using strategic combinations of cheap 7-9B models.