Asymmetric Attention Heads: Structured Head-Wise Context Allocation for Transformer Attention
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Abstract
The paper introduces Asymmetric Attention Heads (AAH), a framework for allocating context length per attention head in transformers, improving performance by allowing heads to focus on different contextual roles.
Reality Card
AAH achieves lower validation loss than standard multi-head attention by allowing heads to utilize different context lengths tailored to their specific roles.
In experiments with 4096-token inputs, AAH-style local-allocation variants demonstrated lower validation loss compared to pure full attention.
The study may face limitations in reproducibility due to the complexity of the hierarchical grouping and allocation mechanisms.
Paper to code
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