Papers › Reducing Transformer Depth on Demand with Structured Dropout
Reducing Transformer Depth on Demand with Structured Dropout
Angela Fan, Edouard Grave, Armand Joulin
Overparameterized transformer networks have obtained state of the art results in various natural language processing tasks, such as machine translation, language modeling, and question answering. These models contain hundreds of millions of parameters, necessitating a large amount of computation and making them prone to overfitting. In this work, we explore LayerDrop, a form of structured dropout, which has a regularization effect during training and allows for efficient pruning at inference time. In particular, we show that it is possible to select sub-networks of any depth from one large network without having to finetune them and with limited impact on performance. We demonstrate the effectiveness of our approach by improving the state of the art on machine translation, language modeling, summarization, question answering, and language understanding benchmarks. Moreover, we show that our approach leads to small BERT-like models of higher quality compared to training from scratch or using distillation.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Open-Domain Question Answering | ELI5 | Transformer Multitask + LayerDrop | Rouge-1 | 29.4 | #5 of 6 | Archive leaderboard | report |
| Open-Domain Question Answering | ELI5 | Transformer Multitask + LayerDrop | Rouge-2 | 5.5 | #5 of 6 | Archive leaderboard | report |
| Open-Domain Question Answering | ELI5 | Transformer Multitask + LayerDrop | Rouge-L | 23.4 | #5 of 6 | 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: LayerDrop
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