{"id":"https://openalex.org/W7166520644","doi":"https://doi.org/10.48550/arxiv.2606.28062","title":"Single and Multi Truth Data Fusion using Large Language Models","display_name":"Single and Multi Truth Data Fusion using Large Language Models","publication_year":2026,"publication_date":"2026-06-26","ids":{"openalex":"https://openalex.org/W7166520644","doi":"https://doi.org/10.48550/arxiv.2606.28062"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.28062","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28062","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.28062","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015463363","display_name":"Hanife Kucuk","orcid":"https://orcid.org/0009-0002-4829-2156"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kucuk, Hira Beril","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066619159","display_name":"Norman W. Paton","orcid":"https://orcid.org/0000-0003-2008-6617"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paton, Norman W","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139600586","display_name":"Jiaoyan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jiaoyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139628332","display_name":"Zhenyu Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zhenyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.2190999984741211,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.2190999984741211,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11719","display_name":"Data Quality and Management","score":0.19429999589920044,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.12030000239610672,"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/truth-value","display_name":"Truth value","score":0.5540000200271606},{"id":"https://openalex.org/keywords/codebase","display_name":"Codebase","score":0.5454000234603882},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5408999919891357},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5213000178337097},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5196999907493591},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5080000162124634},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.49129998683929443},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.47999998927116394}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7245000004768372},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5623000264167786},{"id":"https://openalex.org/C46274116","wikidata":"https://www.wikidata.org/wiki/Q185521","display_name":"Truth value","level":2,"score":0.5540000200271606},{"id":"https://openalex.org/C51929080","wikidata":"https://www.wikidata.org/wiki/Q2425187","display_name":"Codebase","level":3,"score":0.5454000234603882},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5408999919891357},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5213000178337097},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5196999907493591},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5080000162124634},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5037999749183655},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.49129998683929443},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.47999998927116394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38499999046325684},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3750999867916107},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32749998569488525},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C100463513","wikidata":"https://www.wikidata.org/wiki/Q5227322","display_name":"Data model (GIS)","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.28062","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28062","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.28062","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28062","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.48632773756980896,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Data":[0,38],"fusion,":[1],"also":[2],"known":[3],"as":[4,121],"truth":[5,117],"discovery,":[6],"is":[7],"a":[8],"data":[9,77],"integration":[10],"problem":[11],"that":[12,111],"aims":[13],"to":[14,42],"determine":[15],"the":[16,69],"correct":[17,54],"value":[18],"or":[19],"set":[20],"of":[21,26,71,130],"values":[22,34,61],"for":[23,80],"each":[24,49],"attribute":[25,50],"an":[27],"object":[28],"when":[29],"presented":[30],"with":[31],"potentially":[32],"conflicting":[33],"from":[35],"multiple":[36,60],"sources.":[37],"fusion":[39,78],"tasks":[40,79],"belong":[41],"two":[43],"main":[44],"categories:":[45],"single-truth":[46,88],"scenarios,":[47,58,91],"where":[48,59],"has":[51,133],"only":[52],"one":[53],"value,":[55],"and":[56,89,98,123],"multi-truth":[57,90],"can":[62],"be":[63],"valid":[64],"simultaneously.":[65],"This":[66],"paper":[67],"investigates":[68],"use":[70],"Large":[72],"Language":[73],"Models":[74],"(LLMs)":[75],"in":[76],"tabular":[81],"data.":[82],"Various":[83],"prompting":[84],"strategies,":[85],"encompassing":[86],"both":[87],"are":[92,101],"investigated":[93],"empirically.":[94],"Domain-dependent,":[95],"domain-independent,":[96],"zero-shot":[97],"one-shot":[99],"prompts":[100],"evaluated":[102],"on":[103,138],"three":[104],"different":[105],"benchmark":[106],"datasets.":[107,127],"Experimental":[108],"results":[109],"demonstrate":[110],"LLM-based":[112],"approaches":[113],"outperform":[114],"traditional":[115],"unsupervised":[116],"discovery":[118],"methods,":[119],"such":[120],"DART":[122],"LTM,":[124],"across":[125],"all":[126],"The":[128],"codebase":[129],"this":[131],"study":[132],"been":[134],"made":[135],"publicly":[136],"available":[137],"GitHub.":[139]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-30T00:00:00"}