Google DeepMind Introduces a Parameter-Efficient Expert Retrieval Mechanism that Leverages the Product Key Technique for Sparse Retrieval from a Million Tiny Experts
What challenges do wider FFW layers present in transformer models?
Wider feedforward (FFW) layers in transformer models increase computational costs and activation memory linearly, posing scaling challenges. This hampers the feasibility of deploying large-scale models in real-world applications like language modeling and natural language processing tasks, making it essential to address this issue for AI research advancement.
How do MoE architectures reduce computational costs?
MoE architectures reduce computational costs by activating only a subset of experts for each input instead of the entire model4. This approach allows for scalability and adaptability, making MoE suitable for large-scale and diverse datasets4. The sparse activation of experts enables efficient utilization of computational resources.
What limitations do existing MoE models face?
Existing MoE models face limitations such as computational and optimization challenges when scaling beyond a small number of experts. The efficiency gains often plateau with increasing model size due to a fixed number of training tokens1. These limitations prevent the full potential of MoEs from being realized, especially in tasks requiring extensive and continual learning.