{"id":"https://openalex.org/W7134249933","doi":"https://doi.org/10.48550/arxiv.2603.05996","title":"Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL","display_name":"Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7134249933","doi":"https://doi.org/10.48550/arxiv.2603.05996"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.05996","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05996","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.05996","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101730153","display_name":"Bingfeng Chen","orcid":"https://orcid.org/0000-0002-9449-5424"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Bingfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043965965","display_name":"Shaobin Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Shaobin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128568819","display_name":"Yongqi Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Yongqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128535435","display_name":"Boyan Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Boyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076948208","display_name":"Ruichu Cai","orcid":"https://orcid.org/0000-0001-8972-167X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Ruichu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128390647","display_name":"Zhifeng Hao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, Zhifeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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/T10028","display_name":"Topic Modeling","score":0.27489998936653137,"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":0.27489998936653137,"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":0.25679999589920044,"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/T12377","display_name":"Digital Humanities and Scholarship","score":0.09019999951124191,"subfield":{"id":"https://openalex.org/subfields/1208","display_name":"Literature and Literary Theory"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.7724000215530396},{"id":"https://openalex.org/keywords/schema","display_name":"Schema (genetic algorithms)","score":0.7390999794006348},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5144000053405762},{"id":"https://openalex.org/keywords/language-understanding","display_name":"Language understanding","score":0.4375999867916107},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.41990000009536743},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.41530001163482666}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.829200029373169},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7724000215530396},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.7390999794006348},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5738000273704529},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5382000207901001},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5144000053405762},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.4375999867916107},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.41990000009536743},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.41530001163482666},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C30775581","wikidata":"https://www.wikidata.org/wiki/Q632285","display_name":"Database schema","level":3,"score":0.3395000100135803},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31450000405311584},{"id":"https://openalex.org/C2777327318","wikidata":"https://www.wikidata.org/wiki/Q1408390","display_name":"Schema matching","level":3,"score":0.3066999912261963},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.29589998722076416},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C2780660560","wikidata":"https://www.wikidata.org/wiki/Q3951893","display_name":"Schema evolution","level":4,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.05996","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05996","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":"doi:10.48550/arxiv.2603.05996","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05996","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generative":[0],"language":[1,26,55],"models":[2,56],"have":[3],"shown":[4],"significant":[5],"potential":[6],"in":[7,29,40,67,107],"single-turn":[8],"Text-to-SQL.":[9,19,69],"However,":[10],"their":[11],"performance":[12,89],"does":[13],"not":[14],"extend":[15],"equivalently":[16],"to":[17,24,61],"multi-turn":[18,41,68,108],"This":[20],"is":[21],"primarily":[22],"due":[23],"generative":[25,54],"models'":[27],"inadequacy":[28],"handling":[30],"the":[31,91],"complexities":[32],"of":[33],"context":[34],"information":[35],"and":[36,64,77,93,112],"dynamic":[37],"schema":[38,63],"linking":[39],"interactions.":[42],"In":[43],"this":[44],"paper,":[45],"we":[46],"propose":[47],"a":[48,73,78],"framework":[49],"named":[50],"Track-SQL,":[51],"which":[52],"enhances":[53],"with":[57],"dual-extractive":[58],"modules":[59],"designed":[60],"track":[62],"contextual":[65],"changes":[66],"Specifically,":[70],"Track-SQL":[71,86,102],"incorporates":[72],"\\emph{Semantic-enhanced":[74],"Schema":[75],"Extractor}":[76],"\\emph{Schema-aware":[79],"Context":[80],"Extractor}.":[81],"Experimental":[82],"results":[83],"demonstrate":[84],"that":[85,101],"achieves":[87],"state-of-the-art":[88],"on":[90,114],"SparC":[92],"CoSQL":[94],"datasets.":[95],"Furthermore,":[96],"detailed":[97],"ablation":[98],"studies":[99],"reveal":[100],"significantly":[103],"improves":[104],"execution":[105],"accuracy":[106],"interactions":[109],"by":[110],"7.1\\%":[111],"9.55\\%":[113],"these":[115],"datasets,":[116],"respectively.":[117],"Our":[118],"implementation":[119],"will":[120],"be":[121],"open-sourced":[122],"at":[123],"https://github.com/DMIRLAB-Group/Track-SQL.":[124]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-10T00:00:00"}