The Law of Multi-Model Collaboration: Scaling Limits of Model Ensembling for Large Language Models

One-line Summary

This study introduces a scaling law for multi-model collaboration in large language models, showing that diverse model ensembles outperform single models as parameter counts increase.

Plain-language Overview

Large language models have become more powerful by increasing their size and the amount of data they process. However, a single model can only improve so much on its own. This research explores how combining multiple models can lead to even better performance. The authors propose a new scaling law that predicts how well these model ensembles will perform based on their combined size. They find that diverse groups of models work better together than similar models, suggesting that variety is key to improving language model capabilities.

Technical Details