Text-to-SQL: a methodical review of challenges and models

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dc.authorid0009-0003-9031-1485
dc.authorid0000-0002-8649-6013
dc.contributor.authorKanburoğlu, Ali Buğraen_US
dc.contributor.authorTek, Faik Borayen_US
dc.date.accessioned2025-07-25T09:28:00Z
dc.date.available2025-07-25T09:28:00Z
dc.date.issued2024-05-20
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.description.abstractThis survey focuses on Text-to-SQL, automated translation of natural language queries into SQL queries. Initially, we describe the problem and its main challenges. Then, by following the PRISMA systematic review methodology, we survey the existing Text-to-SQL review papers in the literature. We apply the same method to extract proposed Text-to-SQL models and classify them with respect to used evaluation metrics and benchmarks. We highlight the accuracies achieved by various models on Text-to-SQL datasets and discuss execution-guided evaluation strategies. We present insights into model training times and implementations of different models. We also explore the availability of Text-to-SQL datasets in non-English languages. Additionally, we focus on large language model (LLM) based approaches for the Text-to-SQL task, where we examine LLM-based studies in the literature and subsequently evaluate the LLMs on the cross-domain Spider dataset. Finally, we conclude with a discussion of future directions for Text-to-SQL research, identifying potential areas of improvement and advancements in this field.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationKanburoğlu, A. B. & Tek, F. B. (2024). Text-to-SQL: a methodical review of challenges and models. Turkish Journal of Electrical Engineering and Computer Sciences, 32(3), 403-419. doi:10.55730/1300-0632.4077en_US
dc.identifier.doi10.55730/1300-0632.4077
dc.identifier.endpage419
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85195885023
dc.identifier.scopusqualityQ2
dc.identifier.startpage403
dc.identifier.trdizinid1240367
dc.identifier.urihttps://hdl.handle.net/11729/6580
dc.identifier.urihttps://doi.org/10.55730/1300-0632.4077
dc.identifier.volume32
dc.identifier.wosWOS:001235562200002
dc.identifier.wosqualityQ3
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakSobiaden_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorKanburoğlu, Ali Buğraen_US
dc.institutionauthorTek, Faik Borayen_US
dc.institutionauthorid0009-0003-9031-1485
dc.institutionauthorid0000-0002-8649-6013
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherTÜBİTAKen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrencien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectText-to-SQLen_US
dc.subjectLarge language modelen_US
dc.subjectNatural language processingen_US
dc.subjectDeep learningen_US
dc.subjectComputational linguisticsen_US
dc.subjectLarge datasetsen_US
dc.subjectNatural language processing systemsen_US
dc.subjectAutomated translationen_US
dc.subjectLanguage modelen_US
dc.subjectLanguage processingen_US
dc.subjectNatural language queriesen_US
dc.subjectNatural languagesen_US
dc.subjectSQL queryen_US
dc.titleText-to-SQL: a methodical review of challenges and modelsen_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

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