Methods › Natural Language Processing › Autoregressive Transformers › Primer

Primer

14 papers tagged archive 2025-07-28

Introduced by David R. So et al. in Primer: Searching for Efficient Transformers for Language Modeling

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

Primer is a Transformer-based architecture that improves upon the Transformer architecture with two improvements found through neural architecture search: squared RELU activations in the feedforward block, and depthwise convolutions added to the attention multi-head projections: resulting in a new module called Multi-DConv-Head-Attention.

PaperSource

Papers archive 2025-07-28

14 shown of 14, 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

18 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 Modeling4
Language Modelling4
CPU1
Common Sense Reasoning1
Coreference Resolution1
Deep Learning1
Diversity1
Epidemiology1
GPU1
Natural Language Inference1
Protein Structure Prediction1
Question Answering1
Safety Alignment1
Sentiment Analysis1
Specificity1
TAR1
Text Classification1
Word Sense Disambiguation1

Usage over time archive 2025-07-28

Papers per year tagged with Primer: 2021 to 2025, peak 3 3 0 2021: 3 papers 2021 2022: 3 papers 2022 2023: 3 papers 2023 2024: 2 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (14 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

Autoregressive TransformersTransformers

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