{"id":"https://openalex.org/W4312050685","doi":"https://doi.org/10.48550/arxiv.2212.09246","title":"I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation","display_name":"I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation","publication_year":2022,"publication_date":"2022-12-19","ids":{"openalex":"https://openalex.org/W4312050685","doi":"https://doi.org/10.48550/arxiv.2212.09246"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2212.09246","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.09246","pdf_url":"https://arxiv.org/pdf/2212.09246","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2212.09246","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044250030","display_name":"Chandra Bhagavatula","orcid":"https://orcid.org/0000-0001-6264-0378"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bhagavatula, Chandra","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080544237","display_name":"Jena D. Hwang","orcid":"https://orcid.org/0000-0003-3801-294X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hwang, Jena D.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043450042","display_name":"Doug Downey","orcid":"https://orcid.org/0000-0002-4737-8444"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Downey, Doug","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024879161","display_name":"Ronan Le Bras","orcid":"https://orcid.org/0000-0003-2439-6938"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bras, Ronan Le","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102930940","display_name":"Ximing Lu","orcid":"https://orcid.org/0000-0001-6671-4573"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Ximing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Qin, Lianhui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Lianhui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101067919","display_name":"Keisuke Sakaguchi","orcid":"https://orcid.org/0000-0002-3809-1732"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sakaguchi, Keisuke","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076880940","display_name":"Swabha Swayamdipta","orcid":"https://orcid.org/0000-0002-5851-8254"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Swayamdipta, Swabha","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112759123","display_name":"Peter West","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"West, Peter","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102992157","display_name":"Yejin Choi","orcid":"https://orcid.org/0000-0003-3032-5378"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Yejin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5044250030"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9969000220298767,"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":0.9969000220298767,"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":0.9948999881744385,"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/T12090","display_name":"Language and cultural evolution","score":0.9442999958992004,"subfield":{"id":"https://openalex.org/subfields/3316","display_name":"Cultural Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7289934754371643},{"id":"https://openalex.org/keywords/commonsense-reasoning","display_name":"Commonsense reasoning","score":0.7228895425796509},{"id":"https://openalex.org/keywords/commonsense-knowledge","display_name":"Commonsense knowledge","score":0.6393231749534607},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.6297112107276917},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6054847240447998},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.5927538275718689},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5914813280105591},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.513300359249115},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5070526599884033},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4381643533706665},{"id":"https://openalex.org/keywords/argument","display_name":"Argument (complex analysis)","score":0.433624267578125},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4132942259311676},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3229896128177643},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.32014840841293335},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.13634029030799866},{"id":"https://openalex.org/keywords/epistemology","display_name":"Epistemology","score":0.1028364896774292},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.09329575300216675}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7289934754371643},{"id":"https://openalex.org/C193221554","wikidata":"https://www.wikidata.org/wiki/Q5153664","display_name":"Commonsense reasoning","level":2,"score":0.7228895425796509},{"id":"https://openalex.org/C30542707","wikidata":"https://www.wikidata.org/wiki/Q1603203","display_name":"Commonsense knowledge","level":3,"score":0.6393231749534607},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.6297112107276917},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6054847240447998},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.5927538275718689},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5914813280105591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.513300359249115},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5070526599884033},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4381643533706665},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.433624267578125},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4132942259311676},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3229896128177643},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32014840841293335},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.13634029030799866},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.1028364896774292},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.09329575300216675},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2212.09246","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.09246","pdf_url":"https://arxiv.org/pdf/2212.09246","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2212.09246","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2212.09246","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2212.09246","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2212.09246","pdf_url":"https://arxiv.org/pdf/2212.09246","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3035583586","https://openalex.org/W4320165839","https://openalex.org/W2151799802","https://openalex.org/W4385488510","https://openalex.org/W2196562041","https://openalex.org/W2073302931","https://openalex.org/W3206107299","https://openalex.org/W3082691151","https://openalex.org/W4287633646","https://openalex.org/W4378501473"],"abstract_inverted_index":{"Commonsense":[0],"capabilities":[1],"of":[2,46,75,83,91,98,102,127,147,155,202],"pre-trained":[3],"language":[4,36,159],"models":[5,37,42,90],"dramatically":[6],"improve":[7],"with":[8,55,140],"scale,":[9],"leading":[10],"many":[11],"to":[12,65,150,165,198,213],"believe":[13],"that":[14,29,43,70,120,179,205],"scale":[15,180],"is":[16,22,64,181,206],"the":[17,81,96,123,133,136,144,152,156,169,183,207],"only":[18,184],"winning":[19],"recipe.":[20],"But":[21],"it?":[23],"Here,":[24],"we":[25,87],"investigate":[26],"an":[27],"alternative":[28],"a":[30,67,72,115,191,199],"priori":[31],"seems":[32],"impossible:":[33],"can":[34,110,189],"smaller":[35],"(e.g.,":[38,51],"GPT-2)":[39],"win":[40],"over":[41],"are":[44],"orders":[45],"magnitude":[47],"larger":[48],"and":[49,161,209],"better":[50],"GPT-3),":[52],"if":[53],"powered":[54],"novel":[56,116,145,187],"commonsense":[57,76,92,103,117,173],"distillation":[58,118],"algorithms?":[59],"The":[60],"key":[61],"intellectual":[62],"challenge":[63],"design":[66],"learning":[68,164],"algorithm":[69],"achieve":[71],"competitive":[73],"level":[74],"acquisition,":[77],"without":[78],"relying":[79],"on":[80,95,135],"benefits":[82],"scale.":[84],"In":[85],"particular,":[86],"study":[88,196],"generative":[89],"knowledge,":[93],"focusing":[94],"task":[97],"generating":[99],"generics,":[100,203],"statements":[101],"facts":[104],"about":[105],"everyday":[106],"concepts,":[107],"e.g.,":[108],"birds":[109],"fly.":[111],"We":[112],"introduce":[113],"I2D2,":[114],"framework":[119],"loosely":[121],"follows":[122],"Symbolic":[124],"Knowledge":[125],"Distillation":[126],"West":[128],"et":[129],"al.":[130],"but":[131],"breaks":[132],"dependence":[134],"extreme-scale":[137],"teacher":[138],"model":[139],"two":[141],"innovations:":[142],"(1)":[143],"adaptation":[146],"NeuroLogic":[148],"Decoding":[149],"enhance":[151],"generation":[153],"quality":[154,211],"weak,":[157],"off-the-shelf":[158],"models,":[160],"(2)":[162],"self-imitation":[163],"iteratively":[166],"learn":[167],"from":[168],"model's":[170],"own":[171],"enhanced":[172],"acquisition":[174],"capabilities.":[175],"Empirical":[176],"results":[177],"suggest":[178],"not":[182],"way,":[185],"as":[186],"algorithms":[188],"be":[190],"promising":[192],"alternative.":[193],"Moreover,":[194],"our":[195],"leads":[197],"new":[200],"corpus":[201],"Gen-A-tomic,":[204],"largest":[208],"highest":[210],"available":[212],"date.":[214]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2025-10-10T00:00:00"}