MH-MoE: Multi-Head Mixture-of-Experts

• URL: http://arxiv.org/abs/2411.16205v2
• Date: Tue, 26 Nov 2024 06:28:54 GMT
• Title: MH-MoE: Multi-Head Mixture-of-Experts
• Authors: Shaohan Huang, Xun Wu, Shuming Ma, Furu Wei,
• Abstract summary: 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. We present a novel implementation of MH-MoE that maintains both FLOPs and parameter parity with sparse Mixture of Experts models.
• Score: 119.47867308669764
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• 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.

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