MH-MoE:Multi-Head Mixture-of-Experts
Shaohan Huang, Xun Wu, Shuming Ma, Furu Wei
2024-11-26
Summary
This paper introduces Multi-Head Mixture-of-Experts (MH-MoE), a new model that improves how AI can process information by using multiple 'experts' to better understand and analyze data.
What's the problem?
Traditional models that use a technique called Mixture of Experts (MoE) often have problems with low expert activation, meaning only a few experts are used when processing data. This can limit the model's ability to learn and understand complex tasks. Additionally, these models struggle to analyze detailed information within individual pieces of data, which is crucial for tasks like language understanding.
What's the solution?
MH-MoE addresses these issues by employing a multi-head mechanism that splits each input into several smaller pieces called sub-tokens. These sub-tokens are then processed by different experts at the same time, allowing the model to utilize more of its available experts effectively. This approach not only increases the number of active experts but also enhances the model's understanding of subtle differences in the data. The authors conducted experiments showing that MH-MoE outperforms traditional MoE models in various language tasks while maintaining similar computational efficiency.
Why it matters?
This research is important because it enhances the capabilities of AI models, making them better at understanding complex information and improving their performance across different tasks. By increasing expert activation and allowing for finer analysis, MH-MoE can lead to more effective AI applications in areas like natural language processing, making AI tools more powerful and versatile.
Abstract
Multi-Head Mixture-of-Experts (MH-MoE) demonstrates superior performance by using the multi-head mechanism to collectively attend to information from various representation spaces within different experts. In this paper, we present a novel implementation of MH-MoE that maintains both FLOPs and parameter parity with sparse Mixture of Experts models. Experimental results on language models show that the new implementation yields quality improvements over both vanilla MoE and fine-grained MoE models. Additionally, our experiments demonstrate that MH-MoE is compatible with 1-bit Large Language Models (LLMs) such as BitNet.