Methods › Natural Language Processing › Autoencoding Transformers › Longformer

Longformer

87 papers tagged archive 2025-07-28

Introduced by Iz Beltagy et al. in Longformer: The Long-Document Transformer

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

Longformer is a modified Transformer architecture. Traditional Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this, Longformer uses an attention pattern that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. The attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention.

The attention patterns utilised include: sliding window attention, dilated sliding window attention and global + sliding window. These can be viewed in the components section of this page.

PaperSource

Papers archive 2025-07-28

30 shown of 87, 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

20 shown of 111 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 Modelling13
Decoder11
Language Modeling11
Sentence11
Document Classification10
Question Answering9
Abstractive Text Summarization8
Articles7
Classification6
Text Classification6
Natural Language Inference5
Text Summarization5
text-classification5
Retrieval4
Document Summarization3
GPU3
Information Retrieval3
Named Entity Recognition3
Transfer Learning3
named-entity-recognition3

Usage over time archive 2025-07-28

Papers per year tagged with Longformer: 2020 to 2025, peak 28 28 0 2020: 7 papers 2020 2021: 20 papers 2021 2022: 17 papers 2022 2023: 28 papers 2023 2024: 11 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (87 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

Autoencoding TransformersTransformers

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