AT-MoE: Adaptive Task-planning Mixture of Experts via LoRA Approach
This addresses the problem of improving precision and interpretability in complex tasks for domains such as medicine, though it appears incremental as it builds on existing MoE and LoRA methods.
The paper tackles the limitations of existing Mixture of Experts models in task-specific learning and interpretability, particularly in critical fields like medicine, by introducing AT-MoE, which uses LoRA-trained experts and an adaptive grouped routing module to enhance performance and interpretability.
The advent of Large Language Models (LLMs) has ushered in a new era of artificial intelligence, with the potential to transform various sectors through automation and insightful analysis. The Mixture of Experts (MoE) architecture has been proposed as a solution to enhance model performance in complex tasks. Yet, existing MoE models struggle with task-specific learning and interpretability, especially in fields like medicine where precision is critical. This paper introduces the Adaptive Task-planing Mixture of Experts(AT-MoE), an innovative architecture designed to address these limitations. We first train task-specific experts via LoRA approach to enhance problem-solving capabilities and interpretability in specialized areas. Subsequently, we introduce a layer-wise adaptive grouped routing module that optimizes module fusion based on complex task instructions, ensuring optimal task resolution. The grouped routing module first perform overall weight allocation from the dimension of the expert group, and then conduct local weight normalization adjustments within the group. This design maintains multi-dimensional balance, controllability, and interpretability, while facilitating task-specific fusion in response to complex instructions.