Expert-Token Resonance: Redefining MoE Routing through Affinity-Driven Active Selection

• URL: http://arxiv.org/abs/2406.00023v2
• Date: Fri, 30 Aug 2024 11:32:48 GMT
• Title: Expert-Token Resonance: Redefining MoE Routing through Affinity-Driven Active Selection
• Authors: Jing Li, Zhijie Sun, Dachao Lin, Xuan He, Yi Lin, Binfan Zheng, Li Zeng, Rongqian Zhao, Xin Chen,
• Abstract summary: We introduce a novel framework that redefines MoE routing through affinity-driven active selection. Our theoretical analysis demonstrates that this approach mitigates expert homogenization while enabling substantial capacity boundary reduction. After supervised fine-tuning, it exhibits performance gains of 9.7% to 14.1% across GDAD, C-Eval, and TeleQnA benchmarks.
• Score: 16.539855450082946
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Mixture-of-Experts (MoE) architectures have emerged as a paradigm-shifting approach for large language models (LLMs), offering unprecedented computational efficiency. However, these architectures grapple with challenges of token distribution imbalance and expert homogenization, impeding optimal semantic generalization. We introduce a novel framework that redefines MoE routing through affinity-driven active selection. The innovations for the framework encompass: (1) A rigorous formulation of expert-token affinity metrics. (2) An adaptive bidirectional selection mechanism leveraging resonance between experts and tokens. (3) Theoretical derivation and experimental evidence of reduced expert capacity bounds under dynamic token distribution evolution. It is also integrated with orthogonal feature extraction module and an optimized loss function for expert localization. Our theoretical analysis demonstrates that this approach mitigates expert homogenization while enabling substantial capacity boundary reduction. Experimental validation corroborates these findings: it achieves a 40% reduction in token processed by each expert without compromising model convergence or efficacy. When coupled with communication optimizations, the training efficiency improvements of 5.4% to 46.6% can be observed. After supervised fine-tuning, it exhibits performance gains of 9.7% to 14.1% across GDAD, C-Eval, and TeleQnA benchmarks.

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