{"id":"https://openalex.org/W7160635711","doi":"https://doi.org/10.48550/arxiv.2605.06290","title":"Data Language Models: A New Foundation Model Class for Tabular Data","display_name":"Data Language Models: A New Foundation Model Class for Tabular Data","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160635711","doi":"https://doi.org/10.48550/arxiv.2605.06290"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.06290","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06290","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,"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.2605.06290","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135709527","display_name":"Eda Erol","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Erol, Eda","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135660923","display_name":"Giuliano Pezzoli","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pezzoli, Giuliano","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135651786","display_name":"Ozer Cem Kelahmet","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kelahmet, Ozer Cem","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/T12535","display_name":"Machine Learning and Data Classification","score":0.20499999821186066,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.20499999821186066,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.11739999800920486,"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"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.05609999969601631,"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/raw-data","display_name":"Raw data","score":0.5983999967575073},{"id":"https://openalex.org/keywords/serialization","display_name":"Serialization","score":0.4975999891757965},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4837999939918518},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.4763000011444092},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.44279998540878296},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4296000003814697},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.42739999294281006},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.37040001153945923}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7833999991416931},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.5983999967575073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5867000222206116},{"id":"https://openalex.org/C52723943","wikidata":"https://www.wikidata.org/wiki/Q1127410","display_name":"Serialization","level":2,"score":0.4975999891757965},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4837999939918518},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4763000011444092},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4296000003814697},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4284999966621399},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.42739999294281006},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41100001335144043},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39480000734329224},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.37040001153945923},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.3621000051498413},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C100463513","wikidata":"https://www.wikidata.org/wiki/Q5227322","display_name":"Data model (GIS)","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.06290","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06290","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,"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.2605.06290","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06290","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,"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Every":[0,40],"major":[1],"data":[2,71,114,137],"modality":[3,28],"now":[4],"has":[5,14,22,262],"a":[6,56,73,95,151,212,246],"foundation":[7,53,84,174],"model":[8,61,85,97,154,251],"that":[9,132,142,208,261],"understands":[10,91,98],"it":[11,187],"natively:":[12],"text":[13],"language":[15,96,200],"models,":[16,20,54,119],"images":[17],"have":[18],"vision":[19],"audio":[21,23],"models.":[24],"Tabular":[25],"data,":[26],"the":[27,50,64,77,82,93,112,129,148,172,230,256,266],"on":[29,116,156,178,202],"which":[30,117],"many":[31],"consequential":[32],"real-world":[33,162],"AI":[34,44,118,123,140,267],"decisions":[35],"are":[36],"made,":[37],"does":[38],"not.":[39],"approach":[41],"to":[42,49],"tabular":[43,52,70,87,113,163,173,250,258],"today,":[45],"from":[46,106,237,265],"gradient-boosted":[47,167],"trees":[48],"latest":[51],"requires":[55],"preprocessing":[57,130],"pipeline":[58],"before":[59],"any":[60,234,244],"can":[62,125,252],"consume":[63],"data.":[65,88],"None":[66],"of":[67,211,233],"them":[68],"understand":[69],"as":[72],"modality.":[74],"We":[75,145],"introduce":[76],"Data":[78],"Language":[79],"Model":[80],"(DLM),":[81],"missing":[83,184,264],"for":[86,220],"A":[89],"DLM":[90],"tables":[92],"way":[94],"sentences:":[99],"natively,":[100],"without":[101],"serialization":[102],"or":[103],"preprocessing,":[104],"directly":[105],"raw":[107,136,238],"cell":[108,239],"values.":[109],"It":[110,228,254],"is":[111,217,255],"layer":[115,260],"agents,":[120],"and":[121,138,161,171,197],"vertical":[122],"applications":[124],"be":[126],"built,":[127],"eliminating":[128],"pipelines":[131],"currently":[133],"stand":[134],"between":[135],"every":[139],"system":[141],"consumes":[143],"it.":[144],"present":[146],"Schema-1,":[147],"first":[149],"DLM:":[150],"140M":[152],"parameter":[153],"trained":[155],"more":[157,218],"than":[158,192,222],"2.3M":[159],"synthetic":[160],"datasets.":[164],"Schema-1":[165],"outperforms":[166],"ensembles,":[168],"AutoML":[169],"stacks,":[170],"models":[175,201],"we":[176],"evaluate":[177],"established":[179],"row-level":[180],"prediction":[181],"benchmarks.":[182],"On":[183],"value":[185],"reconstruction":[186,190],"achieves":[188],"lower":[189],"error":[191],"all":[193],"classical":[194],"statistical":[195],"methods":[196],"frontier":[198],"large":[199],"mean":[203],"performance":[204],"across":[205,243],"conditions,":[206],"establishing":[207],"structural":[209],"understanding":[210,259],"dataset's":[213],"own":[214],"distributional":[215],"geometry":[216],"useful":[219],"imputation":[221],"world":[223],"knowledge":[224],"encoded":[225],"in":[226],"language.":[227],"identifies":[229],"industry":[231],"sector":[232],"unseen":[235],"dataset":[236],"values":[240],"alone,":[241],"reliably":[242],"domain,":[245],"task":[247],"no":[248],"prior":[249],"perform.":[253],"native":[257],"been":[263],"stack.":[268]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-09T00:00:00"}