Collective computing of specialized models for strategic collaboration
Combining different large language models (LLMs) to enhance performance is a significant challenge in artificial intelligence. When disparate models are merged, their combined effectiveness often decreases. While techniques like model merging, Mixture-of-Experts, and stacking exist, there's been no comprehensive approach to effectively integrate a diverse range of models.
To address this, Model-GLUE has been introduced as a holistic guideline for scaling LLMs. It begins by benchmarking existing scaling techniques, focusing on selective merging and various mixtures. Using insights from these benchmarks, Model-GLUE formulates a strategy to select and aggregate different models characterized by varied architectures and initializations.
The methodology - based on heuristic and evolutionary-like learning - involves:
Experiments using a diverse set of Llama-2-based models showed that Model-GLUE achieved an average performance improvement of 5.61% without any additional training. This approach paves the way for creating more resilient and efficient AI systems by emulating natural, evolutionary processes.
This framework aligns seamlessly with our long-term purpose: to harness nature's resilience through evolutionary-like computing. By clustering mergeable models and selecting optimal merging strategies—much like natural selection and evolution—we are building upon this work to develop AI systems that not only perform better but also exhibit life-like adaptability and robustness. This evolutionary approach to AI model integration allows us to build interconnected domains where AI models evolve, adapt, and improve over time, effectively emulating the properties of natural ecosystems.
"Together, let's simulate digital worlds, master resilience, and decode complexity"
"Where nature's resilence meets intelligent systems"