{"id":"https://openalex.org/W7156057422","doi":"https://doi.org/10.1145/3774904.3792212","title":"SQL-Checker: Error Detection and Labeling for Text-to-SQL with Interpretability Analysis","display_name":"SQL-Checker: Error Detection and Labeling for Text-to-SQL with Interpretability Analysis","publication_year":2026,"publication_date":"2026-04-12","ids":{"openalex":"https://openalex.org/W7156057422","doi":"https://doi.org/10.1145/3774904.3792212"},"language":null,"primary_location":{"id":"doi:10.1145/3774904.3792212","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792212","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774904.3792212","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103232001","display_name":"Xingyu Ma","orcid":"https://orcid.org/0009-0009-6473-0436"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingyu Ma","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0008-6870-6030","affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134734583","display_name":"Xin Tian","orcid":"https://orcid.org/0009-0009-2393-4758"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xin Tian","raw_affiliation_strings":["Wuhan AI Research, Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0009-2393-4758","affiliations":[{"raw_affiliation_string":"Wuhan AI Research, Wuhan, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113393700","display_name":"Lingxiang Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingxiang Wu","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9346-3597","affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210094879","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101619658","display_name":"Xuepeng Wang","orcid":"https://orcid.org/0000-0002-6015-8139"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuepeng Wang","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7979-1348","affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210094879","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101790669","display_name":"Xueming Tang","orcid":"https://orcid.org/0000-0003-0806-5100"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueming Tang","raw_affiliation_strings":["Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-0806-5100","affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058420913","display_name":"Jinqiao Wang","orcid":"https://orcid.org/0000-0002-9118-2780"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinqiao Wang","raw_affiliation_strings":["Insitute of automation, Chinese Academy of Science, Beijing, China, School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, Wuhan AI Research, Wuhan, China, and Peng Cheng Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-9118-2780","affiliations":[{"raw_affiliation_string":"Insitute of automation, Chinese Academy of Science, Beijing, China, School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, Wuhan AI Research, Wuhan, China, and Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210100255"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"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":"2025","last_page":"2036"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12479","display_name":"Web Application Security Vulnerabilities","score":0.18979999423027039,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12479","display_name":"Web Application Security Vulnerabilities","score":0.18979999423027039,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.13519999384880066,"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/T11719","display_name":"Data Quality and Management","score":0.10620000213384628,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.6420999765396118},{"id":"https://openalex.org/keywords/sql","display_name":"SQL","score":0.6144000291824341},{"id":"https://openalex.org/keywords/error-detection-and-correction","display_name":"Error detection and correction","score":0.5813000202178955},{"id":"https://openalex.org/keywords/data-definition-language","display_name":"Data definition language","score":0.4212999939918518},{"id":"https://openalex.org/keywords/stored-procedure","display_name":"Stored procedure","score":0.40529999136924744},{"id":"https://openalex.org/keywords/syntax","display_name":"Syntax","score":0.3659000098705292},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3544999957084656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8476999998092651},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6420999765396118},{"id":"https://openalex.org/C510870499","wikidata":"https://www.wikidata.org/wiki/Q47607","display_name":"SQL","level":2,"score":0.6144000291824341},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.5813000202178955},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5544999837875366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4332999885082245},{"id":"https://openalex.org/C55596503","wikidata":"https://www.wikidata.org/wiki/Q1431648","display_name":"Data definition language","level":3,"score":0.4212999939918518},{"id":"https://openalex.org/C154420247","wikidata":"https://www.wikidata.org/wiki/Q846619","display_name":"Stored procedure","level":5,"score":0.40529999136924744},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.382999986410141},{"id":"https://openalex.org/C60048249","wikidata":"https://www.wikidata.org/wiki/Q37437","display_name":"Syntax","level":2,"score":0.3659000098705292},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.34290000796318054},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3059000074863434},{"id":"https://openalex.org/C11742125","wikidata":"https://www.wikidata.org/wiki/Q1195374","display_name":"Syntax error","level":4,"score":0.2980000078678131},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.29269999265670776},{"id":"https://openalex.org/C3018824978","wikidata":"https://www.wikidata.org/wiki/Q2894891","display_name":"Error analysis","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C150451098","wikidata":"https://www.wikidata.org/wiki/Q506059","display_name":"SQL injection","level":5,"score":0.26089999079704285},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.25940001010894775}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774904.3792212","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792212","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774904.3792212","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792212","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W4360991127","https://openalex.org/W4389518685","https://openalex.org/W4392348090","https://openalex.org/W4399175046","https://openalex.org/W4400909566","https://openalex.org/W4402671560","https://openalex.org/W4407953095","https://openalex.org/W4409362830"],"related_works":[],"abstract_inverted_index":{"Text-to-SQL":[0,21,65,172],"technology":[1],"converts":[2],"natural":[3],"language":[4,16],"queries":[5],"into":[6,149,170],"SQL":[7,25,40,99,111,141],"statements":[8],"for":[9,64],"database":[10],"retrieval.":[11],"Recent":[12],"advances":[13],"in":[14,74],"large":[15],"models":[17],"(LLMs)":[18],"have":[19],"improved":[20],"performance,":[22],"but":[23],"generated":[24],"often":[26],"contains":[27],"semantic":[28],"or":[29],"syntax":[30],"errors":[31],"that":[32],"degrade":[33],"user":[34],"experience":[35],"and":[36,48,76,101],"system":[37],"stability.":[38],"Existing":[39],"error":[41,52,66,72,87,92,98,105,112,142,146,165],"detection":[42,151,166],"methods":[43],"are":[44],"costly,":[45],"lack":[46],"interpretability,":[47],"do":[49],"not":[50],"support":[51],"labeling.":[53],"To":[54],"overcome":[55],"these":[56,86],"issues,":[57],"we":[58,77,139],"propose":[59],"SQL-Checker":[60,130,159,169],"a":[61,79,96,121],"specialized":[62],"model":[63],"detection.":[67],"We":[68],"first":[69],"analyze":[70],"common":[71],"factors":[73,93],"Text-to-SQL,":[75],"design":[78],"novel":[80],"data":[81,114,128],"synthesis":[82],"framework":[83,90],"based":[84],"on":[85,134,163],"factors.":[88],"This":[89],"simulates":[91],"to":[94,108],"construct":[95],"basic":[97],"data,":[100],"then":[102,132],"using":[103],"an":[104],"analysis":[106,113],"template":[107],"distill":[109],"high-quality":[110],"from":[115],"large-scale":[116],"models.":[117],"For":[118],"complex":[119],"errors,":[120],"self-guided":[122],"iterative":[123],"distillation":[124],"strategy":[125],"further":[126],"enhances":[127],"quality.":[129],"is":[131],"trained":[133],"this":[135],"distilled":[136],"dataset.":[137],"Additionally,":[138],"refine":[140],"labeling":[143],"and,":[144],"integrate":[145],"label":[147],"recognition":[148],"the":[150,171],"task,":[152],"enabling":[153],"macro-level":[154],"cause":[155],"analysis.":[156],"Experiments":[157],"show":[158],"achieves":[160],"state-of-the-art":[161],"results":[162],"multiple":[164],"datasets.":[167],"Incorporating":[168],"pipeline":[173],"also":[174],"improves":[175],"execution":[176],"accuracy.":[177]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-04-28T00:00:00"}
