Tungwong Chi's Blog

The Theoretical Foundation of Distribution Mixtures and the Universal Approximation Capability of Mixture-of-Experts Architectures
This article delves into the foundational role of mixture distributions in probability theory and machine learning, demonstrating that arbitrarily complex distributions can be approximated by weighted combinations of simple distributions. Building on this theory, we further analyze how the Mixture-of-Experts (MoE) architecture extends this idea to function approximation, becoming a powerful paradigm for handling complex problems. Through theoretical derivations, case studies, and practical applications, the article systematically explains the mathematical foundations and practical value of this framework.

Multi role-playing large language model
Role-playing in conversational AI allows for the simulation of various characters and scenarios, providing rich and diverse interactions. We propose mRP-LLM, a novel approach that integrates multiple role-playing characters into a single model to maximize resource efficiency and expand the model's conversational capabilities.