{"id":"https://openalex.org/W4385570924","doi":"https://doi.org/10.18653/v1/2023.findings-acl.321","title":"Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text","display_name":"Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4385570924","doi":"https://doi.org/10.18653/v1/2023.findings-acl.321"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2023.findings-acl.321","doi":"https://doi.org/10.18653/v1/2023.findings-acl.321","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.findings-acl.321","pdf_url":"https://aclanthology.org/2023.findings-acl.321.pdf","source":{"id":"https://openalex.org/S4363605144","display_name":"Findings of the Association for Computational Linguistics: ACL (Findings of ACL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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 2023","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2023.findings-acl.321.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084517969","display_name":"Zhun Yang","orcid":"https://orcid.org/0000-0002-9043-5774","observed_orcids":["https://orcid.org/0000-0002-9043-5774"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhun Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076453478","display_name":"Adam Ishay","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adam Ishay","raw_affiliation_strings":["Arizona State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100343841","display_name":"Joohyung Lee","orcid":"https://orcid.org/0000-0003-1102-3905","observed_orcids":["https://orcid.org/0000-0003-1102-3905"]},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]},{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["KR","US"],"is_corresponding":false,"raw_author_name":"Joohyung Lee","raw_affiliation_strings":["Arizona State University","Samsung Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]},{"raw_affiliation_string":"Samsung Research","institution_ids":["https://openalex.org/I2250650973"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.1605,"has_fulltext":true,"cited_by_count":72,"citation_normalized_percentile":{"value":0.98357763,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"5186","last_page":"5219"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11010","display_name":"Logic, Reasoning, and Knowledge","score":0.7842000126838684,"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/T11010","display_name":"Logic, Reasoning, and Knowledge","score":0.7842000126838684,"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/T10028","display_name":"Topic Modeling","score":0.10409999638795853,"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":0.06870000064373016,"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/large-language-models","display_name":"large language models","score":0.9769999980926514},{"id":"https://openalex.org/keywords/logic-programming","display_name":"logic programming","score":0.8709999918937683},{"id":"https://openalex.org/keywords/answer-set-programming","display_name":"answer set programming","score":0.9520000219345093},{"id":"https://openalex.org/keywords/semantic-parsing","display_name":"semantic parsing","score":0.7760000228881836},{"id":"https://openalex.org/keywords/few-shot-learning","display_name":"few-shot learning","score":0.8930000066757202},{"id":"https://openalex.org/keywords/natural-language-reasoning","display_name":"natural language reasoning","score":0.7080000042915344},{"id":"https://openalex.org/keywords/question-answering","display_name":"question answering","score":0.8209999799728394},{"id":"https://openalex.org/keywords/knowledge-representation","display_name":"knowledge representation","score":0.6370000243186951},{"id":"https://openalex.org/keywords/robot-planning","display_name":"robot planning","score":0.9440000057220459},{"id":"https://openalex.org/keywords/spatial-reasoning","display_name":"spatial reasoning","score":0.492000013589859}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8324002027511597},{"id":"https://openalex.org/C182620335","wikidata":"https://www.wikidata.org/wiki/Q2852531","display_name":"Answer set programming","level":3,"score":0.6093364357948303},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.606117844581604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6028224229812622},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.5647441744804382},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.534899115562439},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5242753028869629},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.4887428283691406},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.4621458947658539},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.45873796939849854},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.44433966279029846},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4393216669559479},{"id":"https://openalex.org/C73301696","wikidata":"https://www.wikidata.org/wiki/Q5469984","display_name":"Formalism (music)","level":3,"score":0.4311802089214325},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4167959690093994},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.414040207862854},{"id":"https://openalex.org/C128838566","wikidata":"https://www.wikidata.org/wiki/Q275603","display_name":"Logic programming","level":2,"score":0.3973059356212616},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","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/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C558565934","wikidata":"https://www.wikidata.org/wiki/Q2743","display_name":"Musical","level":2,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/2023.findings-acl.321","doi":"https://doi.org/10.18653/v1/2023.findings-acl.321","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.findings-acl.321","pdf_url":"https://aclanthology.org/2023.findings-acl.321.pdf","source":{"id":"https://openalex.org/S4363605144","display_name":"Findings of the Association for Computational Linguistics: ACL (Findings of ACL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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 2023","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2307.07696","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.07696","pdf_url":"https://arxiv.org/pdf/2307.07696","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2307.07696","doi":"https://doi.org/10.48550/arxiv.2307.07696","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2307.07696","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/v1/2023.findings-acl.321","doi":"https://doi.org/10.18653/v1/2023.findings-acl.321","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2023.findings-acl.321","pdf_url":"https://aclanthology.org/2023.findings-acl.321.pdf","source":{"id":"https://openalex.org/S4363605144","display_name":"Findings of the Association for Computational Linguistics: ACL (Findings of ACL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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 2023","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G614157625","display_name":"RI: Small: Embracing Deep Neural Networks into Probabilistic Answer Set Programming","funder_award_id":"2006747","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385570924.pdf","grobid_xml":"https://content.openalex.org/works/W4385570924.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1496189301","https://openalex.org/W1525961042","https://openalex.org/W1596557244","https://openalex.org/W2061409643","https://openalex.org/W2091138122","https://openalex.org/W2094734546","https://openalex.org/W2121465811","https://openalex.org/W2163274265","https://openalex.org/W2968398601","https://openalex.org/W2978723076","https://openalex.org/W2999524812","https://openalex.org/W3128862637","https://openalex.org/W3156012351","https://openalex.org/W3165543901","https://openalex.org/W3212682848","https://openalex.org/W4224266081","https://openalex.org/W4224912544","https://openalex.org/W4226352076","https://openalex.org/W4281483047","https://openalex.org/W4385573087","https://openalex.org/W4385574218","https://openalex.org/W7133224126"],"related_works":["https://openalex.org/W4386211255","https://openalex.org/W4385437684","https://openalex.org/W4399037068","https://openalex.org/W4281250694","https://openalex.org/W7159890941","https://openalex.org/W7222315541","https://openalex.org/W4407349603","https://openalex.org/W4388651029","https://openalex.org/W4402669921","https://openalex.org/W4413423671"],"abstract_inverted_index":{"While":[0],"large":[1,41],"language":[2,32,42,56],"models":[3,27],"(LLMs),":[4],"such":[5],"as":[6,46,64],"GPT-3,":[7],"appear":[8],"to":[9,22,102,107,121,152,155,163,182],"be":[10,119],"robust":[11,80],"and":[12,81,139,166],"general,":[13],"their":[14],"reasoning":[15,33],"ability":[16],"is":[17,154,162,176,189,196],"not":[18],"at":[19,169,177,199],"a":[20,40,47,59,70,79,99,108,178],"level":[21],"compete":[23],"with":[24,112],"the":[25,104,156,164,185],"best":[26],"trained":[28],"for":[29,66,93],"specific":[30,109],"natural":[31,55],"problems.In":[34],"this":[35,126],"study,":[36],"we":[37],"observe":[38],"that":[39,62,84,117,125,147],"model":[43],"can":[44,53,85,118],"serve":[45],"highly":[48],"effective":[49],"few-shot":[50],"semantic":[51],"parser.It":[52],"convert":[54],"sentences":[57],"into":[58],"logical":[60],"form":[61],"serves":[63],"input":[65],"answer":[67],"set":[68],"programs,":[69],"logic-based":[71],"declarative":[72],"knowledge":[73,115],"representation":[74],"formalism.The":[75],"combination":[76],"results":[77],"in":[78,184],"general":[82],"system":[83],"handle":[86],"multiple":[87,122],"question-answering":[88],"tasks":[89,146],"without":[90],"requiring":[91],"retraining":[92],"each":[94],"new":[95],"task.It":[96],"only":[97],"needs":[98],"few":[100],"examples":[101],"guide":[103],"LLM's":[105],"adaptation":[106],"task,":[110],"along":[111],"reusable":[113],"ASP":[114],"modules":[116],"applied":[120],"tasks.We":[123],"demonstrate":[124],"method":[127],"achieves":[128],"state-of-the-art":[129],"performance":[130],"on":[131],"several":[132],"NLP":[133],"benchmarks,":[134],"including":[135],"bAbI,":[136],"StepGame,":[137],"CLUTRR,":[138],"gSCAN.Additionally,":[140],"it":[141],"successfully":[142],"tackles":[143],"robot":[144],"planning":[145],"an":[148,170],"LLM":[149],"alone":[150],"fails":[151],"solve.C":[153],"top":[157],"right":[158,165],"of":[159,172,193],"D.":[160,194],"C":[161,195],"above":[167,197],"D":[168,198],"angle":[171,181],"about":[173],"45":[174,179],"degrees.C":[175],"degree":[180],"D,":[183],"upper":[186],"righthand":[187],"corner.C":[188],"directly":[190],"north":[191],"east":[192],"2":[200],"o'clock.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":21},{"year":2025,"cited_by_count":32},{"year":2024,"cited_by_count":19}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2023-08-05T00:00:00"}