{"url":"/method/delight","slug":"delight","name":"DeLighT","full_name":"DeLighT","full_name_withheld":false,"description_markdown":"**DeLiGHT** is a [transformer](https://paperswithcode.com/method/transformer) architecture that delivers parameter efficiency improvements by (1) within each Transformer block using [DExTra](https://paperswithcode.com/method/dextra), a deep and light-weight transformation, allowing for the use of [single-headed attention](https://paperswithcode.com/method/single-headed-attention) and bottleneck FFN layers and (2) across blocks using block-wise scaling, that allows for shallower and narrower [DeLighT blocks](https://paperswithcode.com/method/delight-block) near the input and wider and deeper DeLighT blocks near the output.","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/models/delight_transformer.py","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Natural Language Processing","area_id":"natural-language-processing","collection":"Autoregressive Transformers","url":"/methods/category/autoregressive-transformers","pwc_aliases":[]},{"area":"Natural Language Processing","area_id":"natural-language-processing","collection":"Language Models","url":"/methods/category/language-models","pwc_aliases":[]},{"area":"Natural Language Processing","area_id":"natural-language-processing","collection":"Transformers","url":"/methods/category/transformers","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"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}