{"id":"https://openalex.org/W4295793229","doi":"https://doi.org/10.5715/jnlp.29.991","title":"Are Prompt-based Models Clueless?","display_name":"Are Prompt-based Models Clueless?","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4295793229","doi":"https://doi.org/10.5715/jnlp.29.991"},"language":"en","primary_location":{"id":"doi:10.5715/jnlp.29.991","doi":"https://doi.org/10.5715/jnlp.29.991","is_oa":true,"landing_page_url":"https://doi.org/10.5715/jnlp.29.991","pdf_url":"https://www.jstage.jst.go.jp/article/jnlp/29/3/29_991/_pdf","source":{"id":"https://openalex.org/S4210212357","display_name":"Journal of Natural Language Processing","issn_l":"1340-7619","issn":["1340-7619","2185-8314"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Natural Language Processing","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.jstage.jst.go.jp/article/jnlp/29/3/29_991/_pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031104941","display_name":"Pride Kavumba","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]},{"id":"https://openalex.org/I4210110652","display_name":"RIKEN","ror":"https://ror.org/01sjwvz98","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652"]},{"id":"https://openalex.org/I4210126580","display_name":"RIKEN Center for Advanced Intelligence Project","ror":"https://ror.org/03ckxwf91","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652","https://openalex.org/I4210126580"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Pride Kavumba","raw_affiliation_strings":["RIKEN AIP","Tohoku University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN AIP","institution_ids":["https://openalex.org/I4210110652","https://openalex.org/I4210126580"]},{"raw_affiliation_string":"Tohoku University","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015163797","display_name":"Ryo Takahashi","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ryo Takahashi","raw_affiliation_strings":["LegalForce Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LegalForce Research","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Yusuke Oda","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yusuke Oda","raw_affiliation_strings":["LegalForce Research","Tohoku University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LegalForce Research","institution_ids":[]},{"raw_affiliation_string":"Tohoku University","institution_ids":["https://openalex.org/I201537933"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.329,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.55043366,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"29","issue":"3","first_page":"991","last_page":"996"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.7445999979972839,"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":0.7445999979972839,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.11620000004768372,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.08630000054836273,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spurious-correlations","display_name":"spurious correlations","score":0.9890000224113464},{"id":"https://openalex.org/keywords/robustness","display_name":"robustness","score":0.5059999823570251},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.5299999713897705},{"id":"https://openalex.org/keywords/natural-language-inference","display_name":"natural language inference","score":0.7960000038146973},{"id":"https://openalex.org/keywords/fine-tuning","display_name":"fine-tuning","score":0.5860000252723694},{"id":"https://openalex.org/keywords/dataset-bias","display_name":"data bias","score":0.36800000071525574},{"id":"https://openalex.org/keywords/confounding-variables","display_name":"confounding variables","score":0.6700000166893005}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3634112477302551}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5715/jnlp.29.991","doi":"https://doi.org/10.5715/jnlp.29.991","is_oa":true,"landing_page_url":"https://doi.org/10.5715/jnlp.29.991","pdf_url":"https://www.jstage.jst.go.jp/article/jnlp/29/3/29_991/_pdf","source":{"id":"https://openalex.org/S4210212357","display_name":"Journal of Natural Language Processing","issn_l":"1340-7619","issn":["1340-7619","2185-8314"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Natural Language Processing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.5715/jnlp.29.991","doi":"https://doi.org/10.5715/jnlp.29.991","is_oa":true,"landing_page_url":"https://doi.org/10.5715/jnlp.29.991","pdf_url":"https://www.jstage.jst.go.jp/article/jnlp/29/3/29_991/_pdf","source":{"id":"https://openalex.org/S4210212357","display_name":"Journal of Natural Language Processing","issn_l":"1340-7619","issn":["1340-7619","2185-8314"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Natural Language Processing","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4295793229.pdf","grobid_xml":"https://content.openalex.org/works/W4295793229.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W1840435438","https://openalex.org/W2145755360","https://openalex.org/W2607892599","https://openalex.org/W2790415926","https://openalex.org/W2798358706","https://openalex.org/W2951286828","https://openalex.org/W2982944182","https://openalex.org/W3167051856","https://openalex.org/W4281262985"],"related_works":["https://openalex.org/W7144674611","https://openalex.org/W4221142828","https://openalex.org/W3198599617","https://openalex.org/W2982944182","https://openalex.org/W7162763763","https://openalex.org/W4385572526","https://openalex.org/W4224903411","https://openalex.org/W3211360900","https://openalex.org/W4389521024","https://openalex.org/W3185341429"],"abstract_inverted_index":{"Pretrained":[0],"language":[1,12,319,324,327],"models":[2,21,38,175,184,197,219,242,261,282,303,315],"have":[3,98],"achieved":[4],"remarkable":[5,162],"performance":[6,163,240,266],"on":[7,164,267,316],"most":[8],"of":[9,18,66,82,94,135,301,322],"the":[10,15,47,64,79,95,103,116,120,129,136,160,174,196,203,209,212,226,256,287],"natural":[11,323,326],"benchmarks.Until":[13],"recently,":[14],"dominant":[16],"approach":[17],"adapting":[19],"these":[20,37],"to":[22,40,100,122,153,156,170,178,182,191,206,222,237,241,263,274],"downstream":[23,268],"tasks":[24,321],"has":[25,34,234],"been":[26,235],"through":[27],"finetuning":[28,254],"a":[29,132,157,245,251,297],"task-specific":[30,246,253],"head.But,":[31],"previous":[32],"work":[33],"found":[35],"that":[36,185,220,259],"learn":[39,221,273],"exploit":[41,275,283,310],"spurious":[42,59],"correlations":[43,60],"between":[44],"inputs":[45],"and":[46,193,211,304,330],"labels":[48],"(Gururangan":[49],"et":[50,53,56,86,230,293],"al.":[51,54,57,87,231,294],"2018;Poliak":[52],"2018;Kavumba":[55],"2019).These":[58],"may":[61],"exist":[62],"in":[63,115,125,291],"form":[65],"unique":[67,155],"input":[68],"tokens":[69],"or":[70,72,139,306],"style":[71],"annotation":[73],"artifacts,":[74],"which":[75],"all":[76],"fall":[77],"under":[78],"wide":[80],"umbrella":[81],"superficial":[83,89,108,145,150,200,276,284,311],"cues":[84,90,109,146,151],"(Kavumba":[85,229],"2019).While":[88],"are":[91,186,198],"strong":[92],"predictors":[93],"labels,":[96],"they":[97,271,308],"nothing":[99],"do":[101,270],"with":[102],"intended":[104],"task.In":[105],"software":[106],"development,":[107],"can":[110,143,215],"be":[111,123,154,179,223],"viewed":[112],"as":[113,147],"bugs":[114],"task":[117,121],"design.They":[118],"enable":[119],"solved":[124],"non-intended":[126],"ways,":[127],"hence":[128],"dataset":[130,166,210],"becomes":[131],"poor":[133],"benchmark":[134],"required":[137],"ability":[138],"abilities.In":[140],"causality,":[141],"we":[142,189,216,278],"view":[144],"confounding":[148],"variables.Unfortunately,":[149],"tend":[152,176],"single":[158],"dataset.Thus,":[159],"model's":[161],"one":[165],"does":[167],"not":[168,177,307],"transfer":[169],"other":[171],"datasets,":[172],"i.e.,":[173],"robust.In":[180],"order":[181],"develop":[183],"truly":[187],"robust,":[188],"need":[190],"identify":[192],"examine":[194],"if":[195],"exploiting":[199],"cues.This":[201],"is":[202,248,286],"first":[204],"step":[205],"debug":[207],"both":[208],"model's.Only":[213],"then":[214],"train":[217],"robust":[218],"right":[224,227],"for":[225],"reasons":[228],"2021).Recently,":[232],"prompting":[233],"shown":[236],"achieve":[238,264],"competitive":[239],"finetuned":[243],"using":[244,250],"head.This":[247],"despite":[249],"few":[252],"examples.Given":[255],"little":[257],"data":[258],"prompt-based":[260,281,302],"require":[262],"optimal":[265],"tasks,":[269],"also":[272,309],"cues?Thus,":[277],"asked,":[279],"Do":[280],"cues?This":[285],"research":[288],"question":[289],"explored":[290],"Kavumba":[292],"(2022).We":[295],"presented":[296],"rigorous":[298],"empirical":[299],"investigation":[300],"whether":[305],"cues.We":[312],"examined":[313],"prompted-based":[314],"two":[317],"English":[318],"fundamental":[320],"understanding:":[325],"inference":[328],"(NLI)":[329]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2022-09-15T00:00:00"}