Papers › DeLighT: Deep and Light-weight Transformer
DeLighT: Deep and Light-weight Transformer
Sachin Mehta, Marjan Ghazvininejad, Srinivasan Iyer, Luke Zettlemoyer, Hannaneh Hajishirzi
We introduce a deep and light-weight transformer, DeLighT, that delivers similar or better performance than standard transformer-based models with significantly fewer parameters. DeLighT more efficiently allocates parameters both (1) within each Transformer block using the DeLighT transformation, a deep and light-weight transformation, and (2) across blocks using block-wise scaling, which allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output. Overall, DeLighT networks are 2.5 to 4 times deeper than standard transformer models and yet have fewer parameters and operations. Experiments on benchmark machine translation and language modeling tasks show that DeLighT matches or improves the performance of baseline Transformers with 2 to 3 times fewer parameters on average. Our source code is available at: \url{https://github.com/sacmehta/delight}
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | WikiText-103 | DeLighT | Number of params | 99M | #55 of 89 | Archive leaderboard | report |
| Language Modelling | WikiText-103 | DeLighT | Test perplexity | 24.14 | #55 of 89 | Archive leaderboard | report |
| Machine Translation | IWSLT2014 German-English | DeLighT | BLEU score | 35.3 | #23 of 34 | Archive leaderboard | report |
| Machine Translation | WMT2016 English-French | DeLighT | BLEU score | 40.5 | #1 of 1 | Archive leaderboard | report |
| Machine Translation | WMT2016 English-German | DeLighT | BLEU score | 28.0 | #4 of 12 | Archive leaderboard | report |
| Machine Translation | WMT2016 English-Romanian | DeLighT | BLEU score | 34.7 | #1 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: DExTra, DeLighT, DeLighT Block
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