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Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This survey claims that LLM-based text-to-SQL research can be systematically mapped through four paradigms — Pre-Processing, In-Context Learning, Fine-Tuning, and Post-Processing — and uses that map to locate the field's crowded and empty…

desk verdict A useful, current survey of LLM-based text-to-SQL with a sensible four-paradigm taxonomy, but the internal count inconsistencies need fixing before the trend percentages can be trusted. read the letter →

arxiv 2505.23838 v1 pith:TUQF5QKZ submitted 2025-05-28 cs.CL cs.IR

Yiming Huang , Jiyu Guo , Wenxin Mao , Cuiyun Gao , Peiyi Han , Chuanyi Liu , Qing Ling This is my paper
classification cs.CLcs.IR
keywords text-to-SQLlargelanguagemodelsin-contextlearningfine-tuningSQLgenerationdatasetsevaluationmetricssurvey
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a systematic review of text-to-SQL systems built on large language models, covering 122 papers published between April 2022 and October 2024. Its central claim is that the field can be understood through a four-paradigm taxonomy: Pre-Processing, In-Context Learning, Fine-Tuning, and Post-Processing, each broken into subcategories. A sympathetic reader would care because the review turns a fast-growing and scattered literature into a map that shows which techniques are saturated and which are barely explored. It also collects the open datasets and evaluation metrics that define progress in the area, and it names seven challenges and eight future directions.

What carries the argument

The carrying object is the four-paradigm taxonomy, a classification of LLM-based SQL generation methods into Pre-Processing, In-Context Learning, Fine-Tuning, and Post-Processing. The taxonomy organizes the 122 surveyed papers into subcategories such as schema linking, cell value acquisition, question rewriting, data augmentation, supplementary knowledge incorporation, example selection, reasoning enhancement, novel model architectures, model training strategies, direct SQL correction, feedback-guided SQL correction, and output consistency. It does the work of the survey: every trend count, every comparison of how crowded a technique is, and every claim about open problems is computed inside this classification.

What would settle it

Run an independent search using the same five keywords over the same April 2022 to October 2024 window, have two reviewers classify every returned paper into the four paradigms, and compare the corpus and per-category counts against the reported 122 papers; a materially different corpus or a shift in a headline category count would show that the survey's landscape conclusions are not stable.

Watch

Extended reading notes

Core claim

The paper claims that nearly all current LLM-based text-to-SQL work fits into a four-stage SQL-generation workflow. Pre-Processing covers schema linking, cell value acquisition, question rewriting, and data augmentation; In-Context Learning covers supplementary knowledge, example selection, and reasoning enhancement; Fine-Tuning covers novel model architectures and training strategies; and Post-Processing covers direct SQL correction, feedback-guided correction, and output consistency. On this map, the field is lopsided: schema linking (37 papers) and few-shot prompting (40 papers) dominate, while novel fine-tuning architectures (6 papers) and cross-consistency (1 paper) remain thin. The paper further claims that the same map, combined with its survey of datasets and metrics, supports seven named challenges and eight future directions for the field.

Load-bearing premise

The whole map depends on the literature search being complete and on the manual classification of the 122 papers into the four-paradigm taxonomy being correct; the authors themselves acknowledge that relevant studies may have been overlooked and that some papers may be misclassified.

Editorial extensions

If this is right

  • A newcomer to text-to-SQL can use the taxonomy to find the crowded and empty regions of the field: schema linking, few-shot prompting, and feedback-guided correction are heavily studied, whereas novel fine-tuning architectures and cross-consistency are almost untouched.
  • Benchmark practice is concentrated on a few standard datasets and metrics — Spider, BIRD, EX, and TS — while efficiency metrics such as VES/R-VES and robustness metrics such as QVT are newer and less established.
  • The survey's trend data show the field accelerating sharply, with roughly 39% of surveyed methods published between July and October 2024, so the picture is one of rapid change rather than a settled landscape.
  • Seven challenges (ambiguity, schema comprehension, cross-domain generalization, efficiency, data quality, privacy and reliability, and assorted technical issues) and eight future directions follow directly from the taxonomy and are presented as the agenda for the next round of research.
  • Because the taxonomy organizes methods by workflow stage, it gives researchers a shared vocabulary for comparing new systems against existing ones.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • One consequence the paper leaves implicit is that the taxonomy's category counts are not mutually exclusive: a single paper such as DIN-SQL appears in schema linking, task decomposition, example selection, and self-correction, so readers should not sum the subcategory counts to recover the number of papers.
  • If the trend reported here continues, the sparse fine-tuning side of the map is the most likely place for rapid growth, because in-context learning approaches that stuff whole schemas into prompts will hit context-length and cost limits on larger databases.
  • A testable follow-up is to apply the same four-paradigm taxonomy to papers published after October 2024 and check whether the distribution shifts toward fine-tuning, multilingual, and context-dependent text-to-SQL, as the survey's future directions predict.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This manuscript is a systematic literature review of text-to-SQL methods that use large language models. The authors formulate four research questions, describe a retrieval and screening process (Section 3), organize the surveyed papers into a taxonomy with four paradigms (Pre-Processing, In-Context Learning, Fine-Tuning, Post-Processing), tabulate datasets and metrics, and discuss challenges and future directions. The paper claims to analyze 122 relevant articles (with an earlier statement of 92 in the introduction) and makes trend claims about the field as of October 2024.

Significance. If accepted, this survey could serve as a reference for researchers entering the field: the four-paradigm taxonomy is intuitive, the dataset and metric summaries are useful, and the authors are transparent about threats to validity. The paper also explicitly documents inclusion/exclusion criteria and a collaborative screening process, which is good practice for a survey. However, because the paper's quantitative contributions (counts, percentages, trend conclusions) rest on an internally inconsistent enumeration of papers and a classification protocol that is not fully specified, the survey in its current form cannot be considered a reliable quantitative landscape of the field. The taxonomy and qualitative summaries may still be useful after the numeric and methodological issues are repaired.

major comments (4)
  1. [Section 1, Section 3.2.2, Table 3] The paper reports 'analyzing 92 relevant articles' in Section 1 while Section 3.2.2 states 'we gathered 122 relevant articles,' and Table 3 lists 92 methodology, 26 dataset, and 7 metric papers, which sum to 125; the percentages in the same table are computed against 122, so they sum to 102%. These inconsistencies must be reconciled, otherwise the RQ1 distributional claims (venue counts, contribution types, and time trends) are not grounded in a well-defined corpus.
  2. [Figure 4, Tables 4-7, Answer to RQ2] The category counts in the taxonomy are not mutually exclusive but are presented as if they were comparable percentages: the ICL subcategories alone total 135 entries (SKI 14+12+3, ES 20+40, RE 36+10), and the pre-processing table shows overlapping papers (e.g., reference [77] appears in Schema Linking, Cell Value Acquisition, and Question Rewriting). Since the manuscript does not define a multi-label coding scheme, the 'most widely used technique' conclusions in the Answer to RQ2 are not reproducible from the published tables.
  3. [Section 3.2, Section 8.2] The screening and classification process is described only qualitatively. No inter-annotator agreement statistic, no conflict-resolution log, and no operational definitions for edge cases (e.g., when a paper contributes to both pre-processing and in-context learning) are provided. Section 8.2 acknowledges that 'there is a possibility of misunderstanding LLM-based SQL generation techniques, which may result in the misclassification of relevant papers,' but without a protocol the effect of this risk cannot be assessed. This gap undermines the reproducibility of the survey's central classification claim.
  4. [Section 3.2.1-3.2.2] The paper does not report the number of records retrieved from each database, the number of duplicates removed, or the number of papers excluded at each inclusion/exclusion criterion, nor does it provide an explicit enumeration of the 122 included papers separate from the reference list (e.g., as a supplementary CSV). For a systematic review claiming to follow the Petersen et al. guidelines, this prevents an independent audit of the corpus and the reported trend analyses.
minor comments (4)
  1. [Equation (8) in Section 6.2] The fourth case of the R-VES piecewise definition writes '0.75 if ˆy is correct,' but the notation used elsewhere in the formula is 'ˆS_i' for the predicted SQL; this appears to be a typo.
  2. [Figure 3(a) and accompanying text] The rendered axis labels and data labels in Figure 3(a) are garbled (e.g., the sequence '41 81 8' before the axis), making the bimonthly counts hard to read and impossible to verify against the stated 122 total.
  3. [Answer to RQ2 (second bullet)] The text says 'task composition and question decomposition are often utilized,' but the body of Section 5.2 consistently uses the term 'task decomposition'; the phrasing should be harmonized.
  4. [Reference list] Reference [137] is cited as 'arXiv preprint arXiv:2402.148512'; the arXiv identifier appears to contain an extra digit (arXiv IDs are typically 4 digits after the dot, e.g., 2402.14851 or 2402.14852). Please verify.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey organizes external literature into a descriptive taxonomy, and its conclusions are not derived from its own definitions or self-citations.

full rationale

This paper is a systematic literature survey. Its central claims are descriptive: it categorizes 122 external papers into four paradigms (Pre-Processing, In-Context Learning, Fine-Tuning, Post-Processing) and reports counts and trends derived from manual classification. There is no derivation chain in which a predicted quantity is defined in terms of an input, no fitted parameter is renamed as a prediction, and no load-bearing conclusion is justified by a self-citation chain. The authors do cite their own prior works (e.g., DART-SQL [77], TCSR-SQL [142], QDA-SQL [112]), but these are surveyed items among many, and the survey's conclusions do not depend on the correctness of those works. The internal count inconsistencies noted in the paper (e.g., 92 vs 122 articles, Table 3 summing to 125, Figure 4 subcategory counts exceeding 122) and the admitted risk of misclassification in Section 8 are threats to reproducibility and validity, not circularity: they mean the taxonomy may be imperfectly executed, but they do not make the survey's claims equivalent to its inputs. The survey is therefore self-contained as a literature review, and no circular step can be exhibited.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The survey has no mathematical free parameters or invented entities. Its conclusions depend on the completeness of the literature search and on the accuracy of the manual paper classification, both acknowledged as imperfect in Section 8.

assumptions (2)
  • domain assumption The selected databases and keywords capture the relevant LLM-based text-to-SQL literature.
    The review's coverage claims rest on the search procedure in Section 3.2.1; the authors note a moderate risk of missing studies in Section 8.1.
  • domain assumption Manual classification of the 122 papers into the four-paradigm taxonomy is accurate.
    The trend and popularity findings depend on category assignments, and Section 8.2 acknowledges possible misclassification.

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Cite this review

Pith. "Pith review of Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities." pith.science (2026). https://pith.science/paper/TUQF5QKZ

@misc{pith2026250523838,
  author       = {Pith},
  title        = {Pith review of: Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TUQF5QKZ}},
  note         = {Machine review of arXiv:2505.23838}
}
read the original abstract

Converting natural language (NL) questions into SQL queries, referred to as Text-to-SQL, has emerged as a pivotal technology for facilitating access to relational databases, especially for users without SQL knowledge. Recent progress in large language models (LLMs) has markedly propelled the field of natural language processing (NLP), opening new avenues to improve text-to-SQL systems. This study presents a systematic review of LLM-based text-to-SQL, focusing on four key aspects: (1) an analysis of the research trends in LLM-based text-to-SQL; (2) an in-depth analysis of existing LLM-based text-to-SQL techniques from diverse perspectives; (3) summarization of existing text-to-SQL datasets and evaluation metrics; and (4) discussion on potential obstacles and avenues for future exploration in this domain. This survey seeks to furnish researchers with an in-depth understanding of LLM-based text-to-SQL, sparking new innovations and advancements in this field.

Figures

Figures reproduced from arXiv: 2505.23838 by the authors.

Figure 1
Figure 1. An illustrative example of using LLMs for text-to-SQL tasks. Specifically, a user requests LLMs to transform a provided [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The entire process of our systematic survey, including initial literature retrieval, applying inclusion and exclusion [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Publication trends of the surveyed LLM-based text-to-SQL approaches from April 2022 to October 2024. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Overview of the way of organizing surveyed articles. Note that the number in parentheses represents the number of [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Illustration of a typical LLM-based text-to-SQL workflow, involving pre-processing, in-context learning, fine-tuning [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

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Forward citations

Cited by 2 Pith papers

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.