Methods › Natural Language Processing › Autoregressive Transformers › DeLighT
DeLighT
Introduced by Sachin Mehta et al. in DeLighT: Deep and Light-weight Transformer
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
DeLiGHT is a transformer architecture that delivers parameter efficiency improvements by (1) within each Transformer block using DExTra, a deep and light-weight transformation, allowing for the use of single-headed attention and bottleneck FFN layers and (2) across blocks using block-wise scaling, that allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output.
Papers archive 2025-07-28
1 shown of 1, 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.
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DeLighT: Deep and Light-weight Transformer 3 Aug 2020 · 2 repositories · arXiv:2008.00623Syntology ran 0 of 3 samples · 3 unverified
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Language Modeling | 1 |
| Language Modelling | 1 |
| Machine Translation | 1 |
| Translation | 1 |
Usage over time archive 2025-07-28
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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections