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Adaptive Span Transformer

3 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

The Adaptive Attention Span Transformer is a Transformer that utilises an improvement to the self-attention layer called adaptive masking that allows the model to choose its own context size. This results in a network where each attention layer gathers information on their own context. This allows for scaling to input sequences of more than 8k tokens.

Their proposals are based on the observation that, with the dense attention of a traditional Transformer, each attention head shares the same attention span S (attending over the full context). But many attention heads can specialize to more local context (others look at the longer sequence). This motivates the need for a variant of self-attention that allows the model to choose its own context size (adaptive masking - see components).

Source: Adaptive Attention Span in Transformers

Papers archive 2025-07-28

3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Language Modeling2
Language Modelling2
3D Part Segmentation1
8k1
Deep Learning1
Machine Translation1
Translation1

Usage over time archive 2025-07-28

Papers per year tagged with Adaptive Span Transformer: 2019 to 2021, peak 2 2 0 2019: 2 papers 2019 2020: 0 papers 2020 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Transformers

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