<ul><li>MoECollab is a framework that aims to democratize Large Language Model (LLM) development by enabling distributed and collaborative development.</li><li>The framework uses a Mixture of Experts (MoE) architecture to decompose monolithic models into specialized expert modules, allowing diverse contributors to participate regardless of computational resources.</li><li>Experiments show that MoECollab achieves accuracy improvements of 3-7% over baseline models while reducing computational requirements by 34%.</li><li>Expert specialization within MoECollab leads to significant gains in domain-specific tasks, such as achieving improvements of 51-88% F1 score in general classification and 23-44% accuracy in news categorization.</li></ul>

MoECollab: Democratizing LLM Development Through Collaborative Mixture of Experts

Discover more