UMoE: Unifying Attention and FFN with Shared Experts

YUANHANG YANG, Chaozheng Wang, Jing Li

UMoE: Unifying Attention and FFN with Shared Experts: 10 upvotes on Hugging Face Daily Papers, #17 of 24 papers on 2025-05-13. Day-by-day upvote history.

Sparse Mixture of Experts (MoE) architectures have emerged as a promising approach for scaling Transformer models. While initial works primarily incorporated MoE into feed-forward network (FFN) layers, recent studies have explored extending the MoE paradigm to attention layers to enhance model performance. However, existing attention-based MoE layers require specialized implementations and demonstrate suboptimal performance compared to their FFN-based counterparts. In this paper, we aim to unify the MoE designs in attention and FFN layers by introducing a novel reformulation of the attention mechanism, revealing an underlying FFN-like structure within attention modules. Our proposed architecture, UMoE, achieves superior performance through attention-based MoE layers while enabling efficient parameter sharing between FFN and attention components.

Paper page on Hugging Face · arXiv

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