{"url":"/method/delight-block","slug":"delight-block","name":"DeLighT Block","full_name":"DeLighT Block","full_name_withheld":false,"description_markdown":"A **DeLighT Block** is a block used in the [DeLighT](https://paperswithcode.com/method/delight) [transformer](https://paperswithcode.com/method/transformer) architecture. It uses a [DExTra](https://paperswithcode.com/method/dextra) transformation to reduce the dimensionality of the vectors entered into the attention layer, where a [single-headed attention](https://paperswithcode.com/method/single-headed-attention) module is used.  Since the DeLighT block learns wider representations of the input across different layers using DExTra, it enables the authors to replace [multi-head attention](https://paperswithcode.com/method/multi-head-attention) with single-head attention. This is then followed by a light-weight FFN which, rather than expanding the dimension (as in normal Transformers which widen to a dimension 4x the size), imposes a bottleneck and squeezes the dimensions. Again, the reason for this is that the DExTra transformation has already incorporated wider representations so we can squeeze instead at this layer.","description_state":"present","introduced_year":null,"introduced_by":{"title":"DeLighT: Deep and Light-weight Transformer","paper":"/paper/delight-very-deep-and-light-weight","first_author":"Sachin Mehta","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/delight-very-deep-and-light-weight"},"source":{"url":"https://arxiv.org/abs/2008.00623v2","title":"DeLighT: Deep and Light-weight Transformer","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/sacmehta/delight/blob/1197d8ffbcff5c3bfc3b6a040a2ae8af811278c4/fairseq/modules/delight_transformer_layer.py#L19","code_snippet_url_on_a_code_host":true,"categories":[{"area":"General","area_id":"general","collection":"Attention Modules","url":"/methods/category/attention-modules","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/delight-very-deep-and-light-weight","title":"DeLighT: Deep and Light-weight Transformer","date":"2020-08-03","arxiv_id":"2008.00623","n_code_links":2,"syntology":{"ran":0,"of":3,"unverified":3,"pointer_only":0}}],"papers_shown":1,"tasks":[{"task":"/task/language-modeling","name":"Language Modeling","papers":1},{"task":"/task/language-modelling","name":"Language Modelling","papers":1},{"task":"/task/machine-translation","name":"Machine Translation","papers":1},{"task":"/task/translation","name":"Translation","papers":1}],"tasks_shown":4,"n_tasks":4,"usage_by_year":[{"year":"2020","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/delight-block"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}