well-balanced expert utilization. We attribute this gap to a pre-routing bottleneck: multi-head attention concatenates head-specific signals into a single post-attention router input, forcing routing to act on co-occurring feature compositions rather than separable head channels. We show that this router input simultaneously encodes multiple separately d...
Ainslie et al. (2023)Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai.GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints, 2023.URL https://arxiv.org/abs/2305.13245. Akter et al. (2025)Syeda Nahida Akter, Shrimai Prabhumoye, Eric Nyberg, Mostofa...
we propose a novel architecture titled Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation (FAME). We leverage sub-embeddings from each head in the final multi-head attention layer to predict the next item separately, effectively capturing distinct item facets. A gating mechanism then integrates these predictions by ...
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the attention mechanism was improved by introducing a multi-head self-attention mechanism with Q, K, and V weight matrices and increasing the learning parameters to improve the recognition accuracy. All possible label sequences were output to the CRF, and the entity label sequence was generated by CRF decoding. The experimental results sh...
Low expert activation, where only a small subset of experts are activated for optimization. (2) Lacking fine-grained analytical capabilities for multiple semantic concepts within individual tokens. We propose Multi-Head Mixture-of-Experts (MH-MoE), which employs a multi-head mechanism to split each token into multiple sub-tokens. These sub-token...
Cordonnier et al. (2020)Jean-Baptiste Cordonnier, Andreas Loukas, and Martin Jaggi.Multi-head attention: Collaborate instead of concatenate.arXiv preprint arXiv:2006.16362, 2020. Cubuk et al. (2020)Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le.Randaugment: Practical automated data augmentation with a reduced search spac...
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self.attention=nn.MultiheadAttention(embed_dim=512,num_heads=8) self.experts=nn.ModuleList([ExpertNetwork()for_inrange(num_experts)]) defforward(self,x): # 计算专家权重 attn_output,_=self.attention(x,x,x) expert_weights=torch.softmax(attn_output.mean(dim=1),dim=-1) # 动态路由 outputs= fori,expertinenumerate(self.experts): ifexpert_weigh...
