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Summary of Improving Transformers with Dynamically Composable Multi-head Attention, by Da Xiao et al.

Improving Transformers with Dynamically Composable Multi-Head Attention

by Da Xiao, Qingye Meng, Shengping Li, Xingyuan Yuan

First submitted to arxiv on: 14 May 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Computation and Language (cs.CL)

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High Paper authors High Difficulty Summary
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Medium Difficulty Summary
The paper proposes Dynamically Composable Multi-Head Attention (DCMHA), a new attention architecture that addresses the limitations of traditional Multi-Head Attention (MHA) in Transformer models. MHA’s independent attention heads lead to low-rank bottleneck and head redundancy issues. DCMHA solves these problems by introducing a

Keywords

» Artificial intelligence  » Attention  » Machine learning  » Multi head attention  » Perplexity  » Summarization  » Transformer  » Translation  

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