Characterizing the Expressivity of Local Attention in Transformers
The transformer is the most popular neural architecture for language modeling. The cornerstone of the transformer is its global attention mechanism, which lets the model aggregate information from all preceding tokens before generating the next token. One common variant of attention is called local attention, which restricts each token to aggregating information from a bounded window of predecessors, reducing the quadratic cost of global attention to linear. Although this restriction is usually motivated by efficiency, it has also been found to improve model quality, a phenomenon that has so far lacked a satisfactory explanation. We provide a formal account of this phenomenon in terms of ...
| Authors | Jiaoda Li, Ryan Cotterell |
|---|---|
| Primary category | cs.CL |
| Published | 1 May 2026 |
| Last updated at source | 26 June 2026 |
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