{"id":"https://openalex.org/W4385574214","doi":"https://doi.org/10.18653/v1/2022.findings-emnlp.95","title":"Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts","display_name":"Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4385574214","doi":"https://doi.org/10.18653/v1/2022.findings-emnlp.95"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2022.findings-emnlp.95","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2022.findings-emnlp.95","pdf_url":"https://aclanthology.org/2022.findings-emnlp.95.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2022","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2022.findings-emnlp.95.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100341810","display_name":"Xiangyang Liu","orcid":"https://orcid.org/0000-0003-0698-4162"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyang Liu","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Information Processing, Fudan University","School of Computer Science, Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Computer Science, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058308999","display_name":"Tianxiang Sun","orcid":"https://orcid.org/0000-0001-8291-820X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianxiang Sun","raw_affiliation_strings":["School of Computer Science, Fudan University","Shanghai Key Laboratory of Intelligent Information Processing, Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088834359","display_name":"Xuanjing Huang","orcid":"https://orcid.org/0000-0001-9197-9426"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuanjing Huang","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Information Processing, Fudan University","School of Computer Science, Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Computer Science, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044665993","display_name":"Xipeng Qiu","orcid":"https://orcid.org/0000-0001-7163-5247"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xipeng Qiu","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Information Processing, Fudan University","School of Computer Science, Fudan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Computer Science, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2184,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.80769862,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1325","last_page":"1338"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9796000123023987,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lagging","display_name":"Lagging","score":0.8545398116111755},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8093643188476562},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5957913994789124},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5713443756103516},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5479301810264587},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.07153761386871338}],"concepts":[{"id":"https://openalex.org/C2776962539","wikidata":"https://www.wikidata.org/wiki/Q6472078","display_name":"Lagging","level":2,"score":0.8545398116111755},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8093643188476562},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5957913994789124},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5713443756103516},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5479301810264587},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.07153761386871338},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2022.findings-emnlp.95","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2022.findings-emnlp.95","pdf_url":"https://aclanthology.org/2022.findings-emnlp.95.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2022","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2022.findings-emnlp.95","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2022.findings-emnlp.95","pdf_url":"https://aclanthology.org/2022.findings-emnlp.95.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2022","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3077791970","display_name":null,"funder_award_id":"2020AAA0106700","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G460263832","display_name":null,"funder_award_id":"62022027","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385574214.pdf"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W131533222","https://openalex.org/W2014902591","https://openalex.org/W2028175314","https://openalex.org/W2114524997","https://openalex.org/W2130158090","https://openalex.org/W2163455955","https://openalex.org/W2251939518","https://openalex.org/W2908510526","https://openalex.org/W2923014074","https://openalex.org/W2963341956","https://openalex.org/W2963748441","https://openalex.org/W2963846996","https://openalex.org/W2964303773","https://openalex.org/W2965373594","https://openalex.org/W2970597249","https://openalex.org/W2979826702","https://openalex.org/W3034999214","https://openalex.org/W3088409176","https://openalex.org/W3120074043","https://openalex.org/W3122890974","https://openalex.org/W3125127048","https://openalex.org/W3168867926","https://openalex.org/W3173777717","https://openalex.org/W3174770825","https://openalex.org/W3176828726","https://openalex.org/W3202031169","https://openalex.org/W3207878700","https://openalex.org/W4205991051","https://openalex.org/W4206178588","https://openalex.org/W4221144361","https://openalex.org/W4224115290","https://openalex.org/W4225619898","https://openalex.org/W4226364033","https://openalex.org/W4285247752","https://openalex.org/W4286905518","https://openalex.org/W4287122891","https://openalex.org/W4288089799","https://openalex.org/W4292779060","https://openalex.org/W4294808066","https://openalex.org/W4295312788","https://openalex.org/W4380609152","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W2021850411","https://openalex.org/W3029477232","https://openalex.org/W4312263439","https://openalex.org/W2747936341","https://openalex.org/W2910365469","https://openalex.org/W1969481115","https://openalex.org/W4313907701","https://openalex.org/W2390459957","https://openalex.org/W3086717436","https://openalex.org/W4286787650"],"abstract_inverted_index":{"Prompt":[0,109],"tuning":[1,5,54,80,176],"is":[2,35,55,135,154],"a":[3,16,83,114,138],"parameter-efficient":[4],"(PETuning)":[6],"method":[7],"for":[8],"utilizing":[9],"pre-trained":[10],"models":[11],"(PTMs)":[12],"that":[13,112,167],"simply":[14],"prepends":[15],"soft":[17],"prompt":[18,26,53,79,95,101,116,134,140,149],"to":[19,27,30,60,92,173],"the":[20,25,49,61,64,75,86,93,97,100,122,126,144,148],"input":[21,127],"and":[22,37,81,96,152,163,177,184,192],"only":[23],"optimizes":[24],"adapt":[28],"PTMs":[29],"downstream":[31],"tasks.":[32],"Although":[33],"it":[34],"parameter-":[36],"deployment-efficient,":[38],"its":[39],"performance":[40,77,172],"still":[41],"lags":[42],"behind":[43],"other":[44,178],"state-of-the-art":[45],"PETuning":[46,179],"methods.":[47],"Besides,":[48],"training":[50,190],"cost":[51],"of":[52,78,99,121,125],"not":[56],"significantly":[57],"reduced":[58],"due":[59],"back-propagation":[62],"through":[63],"entire":[65],"model.":[66],"Through":[67,156],"empirical":[68],"analyses,":[69],"we":[70,106,165],"shed":[71],"some":[72],"light":[73],"on":[74,102,143],"lagging":[76],"recognize":[82],"trade-off":[84],"between":[85],"propagation":[87],"distance":[88],"from":[89],"label":[90],"signals":[91],"inserted":[94],"influence":[98],"model":[103,175],"outputs.":[104],"Further,":[105],"present":[107],"Late":[108],"Tuning":[110],"(LPT)":[111],"inserts":[113],"late":[115,133],"into":[117],"an":[118],"intermediate":[119],"layer":[120,128,151],"PTM":[123],"instead":[124],"or":[129],"all":[130],"layers.":[131],"The":[132],"obtained":[136],"by":[137],"neural":[139],"generator":[141],"conditioned":[142],"hidden":[145],"states":[146],"before":[147],"insertion":[150],"therefore":[153],"instance-dependent.":[155],"extensive":[157],"experimental":[158],"results":[159],"across":[160],"various":[161],"tasks":[162],"PTMs,":[164],"show":[166],"LPT":[168],"can":[169],"achieve":[170],"competitive":[171],"full":[174],"methods":[180],"under":[181],"both":[182],"full-data":[183],"few-shot":[185],"scenarios":[186],"while":[187],"possessing":[188],"faster":[189],"speed":[191],"lower":[193],"memory":[194],"cost.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
