The Dynamic Adaptive Shared Expert and Grouped Multi-Head Attention Hybrid Model (DASG-MoE) addresses significant limitations in existing transformer models used for long-sequence tasks. By employing a Grouped Multi-Head Attention mechanism, the model effectively reduces computational complexity without sacrificing the depth of information processing. This approach facilitates parallel processing through sequence grouping, thereby improving performance and addressing the inherent issues related to long-range dependencies.
Moreover, the Dual-Scale Shared Expert Structure within DASG-MoE allows for a dynamic response to varying feature complexities. Shallow experts are designed for lightweight computations to cater to low-dimensional features, while deeper experts tackle high-dimensional complex semantics. This balance between efficiency and accuracy is further enhanced by a hierarchical Adaptive Dynamic Routing mechanism, ensuring that expert levels are dynamically selected based on specific task requirements. The experimental results indicate that DASG-MoE significantly outperforms current state-of-the-art models, suggesting strong implications for advancements in long-sequence modeling capabilities in various applications.
👉 Pročitaj original: arXiv AI Papers
