{"id":"https://openalex.org/W4225741227","doi":"https://doi.org/10.18653/v1/2022.findings-acl.104","title":"Multi-Stage Prompting for Knowledgeable Dialogue Generation","display_name":"Multi-Stage Prompting for Knowledgeable Dialogue Generation","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4225741227","doi":"https://doi.org/10.18653/v1/2022.findings-acl.104"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2022.findings-acl.104","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2022.findings-acl.104","pdf_url":"https://aclanthology.org/2022.findings-acl.104.pdf","source":{"id":"https://openalex.org/S4363605144","display_name":"Findings of the Association for Computational Linguistics: ACL 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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: ACL 2022","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2022.findings-acl.104.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zihan Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Zihan Liu","raw_affiliation_strings":["The Hong Kong University of Science and Technology,"],"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology,","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031170568","display_name":"Mostofa Patwary","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mostofa Patwary","raw_affiliation_strings":["NVIDIA"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010578786","display_name":"Ryan Prenger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ryan Prenger","raw_affiliation_strings":["NVIDIA"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000188096","display_name":"Shrimai Prabhumoye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shrimai Prabhumoye","raw_affiliation_strings":["NVIDIA"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wei Ping","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Ping","raw_affiliation_strings":["NVIDIA"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072436307","display_name":"Mohammad Shoeybi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammad Shoeybi","raw_affiliation_strings":["NVIDIA"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066242985","display_name":"Bryan Catanzaro","orcid":"https://orcid.org/0000-0003-0034-7728"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bryan Catanzaro","raw_affiliation_strings":["NVIDIA"],"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I200769079"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02724911,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1317","last_page":"1337"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8377000093460083,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8377000093460083,"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/T12031","display_name":"Speech and dialogue systems","score":0.08449999988079071,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.020800000056624413,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.9068814516067505},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8597820997238159},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.7285546660423279},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6883872151374817},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5015304088592529},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.4904727637767792},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46365317702293396},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.44479721784591675},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4415808320045471},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.43018102645874023},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.41607680916786194},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33382275700569153},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.2855800986289978}],"concepts":[{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.9068814516067505},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8597820997238159},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.7285546660423279},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6883872151374817},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5015304088592529},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.4904727637767792},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46365317702293396},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.44479721784591675},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4415808320045471},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.43018102645874023},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.41607680916786194},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33382275700569153},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.2855800986289978},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/2022.findings-acl.104","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2022.findings-acl.104","pdf_url":"https://aclanthology.org/2022.findings-acl.104.pdf","source":{"id":"https://openalex.org/S4363605144","display_name":"Findings of the Association for Computational Linguistics: ACL 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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: ACL 2022","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-119843","is_oa":false,"landing_page_url":"http://www.scopus.com/record/display.url?eid=2-s2.0-85140403127&origin=inward","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"}],"best_oa_location":{"id":"doi:10.18653/v1/2022.findings-acl.104","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2022.findings-acl.104","pdf_url":"https://aclanthology.org/2022.findings-acl.104.pdf","source":{"id":"https://openalex.org/S4363605144","display_name":"Findings of the Association for Computational Linguistics: ACL 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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: ACL 2022","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4225741227.pdf","grobid_xml":"https://content.openalex.org/works/W4225741227.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W193554","https://openalex.org/W6836841","https://openalex.org/W17173102","https://openalex.org/W17875541","https://openalex.org/W6789168","https://openalex.org/W7174772","https://openalex.org/W10064791","https://openalex.org/W10961682","https://openalex.org/W9929840","https://openalex.org/W9888033"],"abstract_inverted_index":{"Existing":[0],"knowledge-grounded":[1],"dialogue":[2,95,108,142],"systems":[3],"typically":[4,20],"use":[5],"finetuned":[6],"versions":[7],"of":[8,27,146],"a":[9,71,80],"pretrained":[10,60,82],"language":[11],"model":[12,125,143,162],"(LM)":[13],"and":[14,31,110,132,149,155,168,182,186],"large-scale":[15],"knowledge":[16,29,56,91,119,130],"bases.":[17],"These":[18],"models":[19],"fail":[21],"to":[22,48,75,89,102,153,164,180,190],"generalize":[23],"on":[24,93,106],"topics":[25],"outside":[26],"the":[28,54,59,87,94,107,111,122,140,174],"base,":[30],"require":[32],"maintaining":[33],"separate":[34],"potentially":[35],"large":[36],"checkpoints":[37],"each":[38],"time":[39],"finetuning":[40],"is":[41],"needed.":[42],"In":[43,134],"this":[44],"paper,":[45],"we":[46,98,159],"aim":[47],"address":[49],"these":[50],"limitations":[51],"by":[52,126,151,178,188],"leveraging":[53],"inherent":[55],"stored":[57],"in":[58,144],"LM":[61,88],"as":[62,64],"well":[63],"its":[65],"powerful":[66],"generation":[67,175],"ability.":[68],"We":[69,84],"propose":[70],"multi-stage":[72,137],"prompting":[73,138],"approach":[74],"generate":[76,90,103],"knowledgeable":[77],"responses":[78,104],"from":[79],"single":[81],"LM.":[83],"first":[85],"prompt":[86,100],"based":[92,105],"context.":[96],"Then,":[97],"further":[99],"it":[101],"context":[109],"previously":[112],"generated":[113],"knowledge.":[114],"Results":[115],"show":[116,169],"that":[117,170],"our":[118,136,161],"generator":[120],"outperforms":[121,139],"state-of-the-art":[123],"retrieval-based":[124],"5.8%":[127],"when":[128],"combining":[129],"relevance":[131],"correctness.":[133],"addition,":[135],"finetuning-based":[141],"terms":[145],"response":[147,183],"knowledgeability":[148,185],"engagement":[150,187],"up":[152,163,179,189],"10%":[154],"5%,":[156],"respectively.":[157],"Furthermore,":[158],"scale":[160],"530":[165],"billion":[166],"parameters":[167],"larger":[171],"LMs":[172],"improve":[173],"correctness":[176],"score":[177],"10%,":[181],"relevance,":[184],"10%.":[191]},"counts_by_year":[],"updated_date":"2026-03-03T08:47:05.690250","created_date":"2022-05-05T00:00:00"}
