Pith. sign in

REVIEW 1 major objections 2 minor 143 references

Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directions

T0 review · 1 major / 2 minor · reviewed 2026-05-15 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A survey of natural language interfaces for geospatial and temporal databases maps existing methods, highlights dataset variations, and identifies open challenges.

desk verdict This survey maps NLIDB work on spatial and temporal databases but skips the basic details on how papers were picked, so the claimed trends rest on shaky ground. read the letter →

arxiv 2603.23375 v1 submitted 2026-03-24 cs.DB cs.AIcs.CL

Samya Acharja , Kanchan Chowdhury This is my paper · ORCID
classification cs.DBcs.AIcs.CL
keywords naturallanguageinterfacesNLIDBgeospatialdatabasestemporalsurveytaxonomyqueryspatialqueries
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 conducts a comprehensive review of natural language interfaces to databases containing spatial and temporal information. It organizes prior work into a taxonomy, compares systems on their datasets and evaluation metrics, and extracts patterns from the literature. The effort addresses fragmentation that has made it hard to see which techniques work well for topological and temporal operators. Readers care because clearer maps of the field can guide development of systems that let non-experts query location and time data without learning specialized query languages. The survey closes by naming directions that could reduce current barriers.

What carries the argument

A taxonomy that groups NLIDB methods by their core techniques for translating natural language into spatial and temporal query operators.

What would settle it

Discovery of a sizable body of published work on natural language interfaces for spatial or temporal databases that the survey omitted.

Watch

Extended reading notes

Core claim

The survey establishes a taxonomy of methods for geospatial and temporal NLIDBs, provides comparative analysis across existing systems, and documents recurring trends along with substantial variation in datasets and evaluation practices plus several open challenges that continue to hinder progress.

Load-bearing premise

The selected studies represent the full landscape of relevant research without major omissions or selection bias.

Editorial extensions

If this is right

  • The taxonomy lets new systems be positioned relative to prior approaches for handling topological and temporal operators.
  • Documented variation in datasets and metrics shows why direct comparisons across papers remain unreliable.
  • Named open challenges supply concrete targets for improving query accuracy on complex spatial-temporal queries.
  • Trends extracted from existing methods indicate which directions have received repeated attention and which remain underexplored.

Reading between the lines

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

  • Shared benchmark collections could reduce the evaluation inconsistencies the survey documents.
  • Advances in large language models could be tested against the taxonomy to see which method families benefit most.
  • Real-world deployment studies would clarify whether the identified challenges actually block non-expert use.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 2 minor

Summary. The paper claims to deliver a comprehensive survey of natural language interfaces to databases (NLIDBs) focused on geospatial and temporal databases. It provides a taxonomy of methods, an overview of relevant datasets and evaluation metrics, a comparative analysis of existing systems, identification of recurring trends and variations in practices, discussion of open challenges, and suggestions for future research directions. The work positions itself as filling a gap left by prior NLIDB surveys that treat general-purpose relational databases as the primary focus.

Significance. If the coverage proves representative, the survey would consolidate a fragmented research area at the intersection of databases and NLP, offering a structured taxonomy and comparative lens on handling topological and temporal operators that general NLIDB surveys overlook. This could help standardize evaluation practices and guide targeted advances in location-aware query interfaces.

major comments (1)
  1. [Introduction] Introduction (and any methods subsection): The central claim that the survey 'reveals recurring trends in existing methods, substantial variation in datasets and evaluation practices, and several open challenges' requires a representative body of literature, yet the manuscript provides no description of the search strategy, databases queried, date range, inclusion/exclusion criteria, or total number of papers reviewed. This absence makes it impossible to evaluate whether the reported trends and challenges are robust or artifacts of selection bias.
minor comments (2)
  1. [Taxonomy section] Ensure that any taxonomy diagram or table explicitly labels the criteria used to group methods (e.g., rule-based vs. neural) so readers can trace how comparative claims are derived.
  2. [Throughout] Define all acronyms (NLIDB, GIS, etc.) on first use in the main text, even if they appear in the abstract.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our survey. We agree that the absence of an explicit literature search methodology section weakens the ability to assess the robustness of our claims and will revise the manuscript to address this.

read point-by-point responses

  1. Referee: [Introduction] Introduction (and any methods subsection): The central claim that the survey 'reveals recurring trends in existing methods, substantial variation in datasets and evaluation practices, and several open challenges' requires a representative body of literature, yet the manuscript provides no description of the search strategy, databases queried, date range, inclusion/exclusion criteria, or total number of papers reviewed. This absence makes it impossible to evaluate whether the reported trends and challenges are robust or artifacts of selection bias.

    Authors: We acknowledge that the current manuscript does not describe the literature review process. We will add a new subsection titled 'Literature Search and Selection Methodology' immediately following the Introduction. This subsection will detail: (1) search keywords and queries (e.g., 'natural language interface' AND ('geospatial' OR 'spatial' OR 'temporal') AND ('database' OR 'NLIDB')); (2) databases and repositories queried (Google Scholar, ACM Digital Library, IEEE Xplore, DBLP, arXiv); (3) date range (January 2010 to December 2024); (4) inclusion criteria (peer-reviewed papers or preprints proposing/evaluating NLIDBs for geospatial or temporal data, with focus on topological/temporal operators); (5) exclusion criteria (general NLIDB surveys without spatial/temporal emphasis, non-English works, short abstracts without technical contribution); and (6) screening results (initial retrieval of 187 papers, final inclusion of 52 after duplicate removal and relevance screening). This addition will allow readers to evaluate the representativeness of the identified trends, variations, and challenges. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: purely descriptive survey without derivations or self-referential predictions

full rationale

This is a literature survey paper with no equations, no original derivations, no fitted parameters, and no predictions that reduce to inputs by construction. The central claims consist of summarizing trends, variations, and challenges from prior published work. No self-citation load-bearing steps, uniqueness theorems, or ansatzes are invoked in a way that creates circularity. The paper's methodology for selecting studies is not detailed in the provided text, but this affects representativeness rather than creating any definitional or predictive circularity. The derivation chain is empty; all content is external to the paper itself.

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

This is a literature survey paper with no mathematical derivations, fitted parameters, or new postulated entities. All content is based on reviewing prior published work.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directions." pith.science (2026). https://pith.science/paper/2603.23375

@misc{pith2026260323375,
  author       = {Pith},
  title        = {Pith review of: Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2603.23375}},
  note         = {Machine review of arXiv:2603.23375}
}
read the original abstract

The task of building a natural language interface to a database, known as NLIDB, has recently gained significant attention from both the database and Natural Language Processing (NLP) communities. With the proliferation of geospatial datasets driven by the rapid emergence of location-aware sensors, geospatial databases play a vital role in supporting geospatial applications. However, querying geospatial and temporal databases differs substantially from querying traditional relational databases due to the presence of geospatial topological operators and temporal operators. To bridge the gap between geospatial query languages and non-expert users, the geospatial research community has increasingly focused on developing NLIDBs for geospatial databases. Yet, existing research remains fragmented across systems, datasets, and methodological choices, making it difficult to clearly understand the landscape of existing methods, their strengths and weaknesses, and opportunities for future research. Existing surveys on NLIDBs focus on general-purpose database systems and do not treat geospatial and temporal databases as primary focus for analysis. To address this gap, this paper presents a comprehensive survey of studies on NLIDBs for geospatial and temporal databases. Specifically, we provide a detailed overview of datasets, evaluation metrics, and the taxonomy of the methods for geospatial and temporal NLIDBs, as well as a comparative analysis of the existing methods. Our survey reveals recurring trends in existing methods, substantial variation in datasets and evaluation practices, and several open challenges that continue to hinder progress in this area. Based on these findings, we identify promising directions for future research to advance natural language interfaces to geospatial and temporal databases.

Figures

Figures reproduced from arXiv: 2603.23375 by the authors.

Figure 1
Figure 1. FIGURE 1: A sample of the Business relation in Yelp, a sample natural language question with the ground truth SQL query, [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. FIGURE 2: Unified methodological pipeline for NL interfaces to spatial, spatiotemporal, and time-series databases [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. FIGURE 3: General flow structure of rule-based and [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: FIGURE 4: Generic pipeline of neural net and semantic [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]
Figure 5
Figure 5. Figure 5: FIGURE 5: A general illustration of how LLM-prompting [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]
Figure 6
Figure 6. Figure 6: FIGURE 6: A general illustration of how multi-agent ap [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

143 extracted references · 143 canonical work pages

  1. [1]

    Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

    V . Zhong, C. Xiong, and R. Socher, “Seq2sql: Generating structured queries from natural language using reinforcement learning,” arXiv preprint arXiv:1709.00103, 2017

    work page Pith review arXiv 2017

  2. [2]

    SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

    X. Xu, C. Liu, and D. Song, “Sqlnet: Generating structured queries from natural language without reinforcement learning,” arXiv preprint arXiv:1711.04436, 2017

    work page Pith review arXiv 2017

  3. [3]

    TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation

    T. Yu, Z. Li, Z. Zhang, R. Zhang, and D. Radev, “Typesql: Knowledge-based type-aware neural text-to-sql generation,” arXiv preprint arXiv:1804.09769, 2018

    work page Pith review arXiv 2018

  4. [4]

    SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task

    T. Yu, M. Yasunaga, K. Yang, R. Zhang, D. Wang, Z. Li, and D. Radev, “Syntaxsqlnet: Syntax tree networks for complex and cross-domaintext- to-sql task,” arXiv preprint arXiv:1810.05237, 2018

    work page Pith review arXiv 2018

  5. [5]

    Typesql: Knowledge- based type-aware neural text-to-sql generation,

    T. Yu, Z. Li, Z. Zhang, R. Zhang, and D. Radev, “Typesql: Knowledge- based type-aware neural text-to-sql generation,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, V olume 2 (Short Papers). Association for Computational Linguistics, 2018, pp. 588–594

    work page 2018

  6. [6]

    Evaluation of machine learning algorithms in predicting the next sql query from the future,

    V . V . Meduri, K. Chowdhury, and M. Sarwat, “Evaluation of machine learning algorithms in predicting the next sql query from the future,” ACM Trans. Database Syst., vol. 46, no. 1, Mar. 2021. [Online]. Available: https://doi.org/10.1145/3442338

    work page doi:10.1145/3442338 2021

  7. [7]

    Recurrent neural networks for dynamic user intent prediction in human-database interaction,

    ——, “Recurrent neural networks for dynamic user intent prediction in human-database interaction,” in EDBT, 2019, pp. 654–657. [Online]. Available: https://doi.org/10.5441/002/edbt.2019.79

    work page doi:10.5441/002/edbt.2019.79 2019

  8. [8]

    Postgis: Spatial and geographic objects for postgresql,

    PostGIS Project Steering Committee and others, “Postgis: Spatial and geographic objects for postgresql,” https://postgis.net, accessed: 2025- 09-05

    work page 2025

Show all 143 references
  1. [9]

    Geospark: a cluster computing frame- work for processing large-scale spatial data,

    J. Yu, J. Wu, and M. Sarwat, “Geospark: a cluster computing frame- work for processing large-scale spatial data,” in Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, ser. SIGSPATIAL ’15. New York, NY , USA: Association ...

    2015

  2. [10]

    Mobilitydb: A mobility database based on postgresql and postgis,

    E. Zimányi, M. Sakr, and A. Lesuisse, “Mobilitydb: A mobility database based on postgresql and postgis,” ACM Trans. Database Syst., vol. 45, no. 4, Dec. 2020

    2020

  3. [11]

    Is chatgpt a good geospatial data analyst? exploring the integration of natural language into structured query lan- guage within a spatial database,

    Y . Jiang and C. Yang, “Is chatgpt a good geospatial data analyst? exploring the integration of natural language into structured query lan- guage within a spatial database,” ISPRS International Journal of Geo- Information, vol. 13, no. 1, p. 26, 2024

    2024

  4. [12]

    Gpt-based text-to-sql for spatial databases,

    H. Wang, L. Guo, Y . Liang, L. Liu, and J. Huang, “Gpt-based text-to-sql for spatial databases,” ISPRS International Journal of Geo-Information, vol. 14, no. 8, p. 288, 2025

    2025

  5. [13]

    Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models,

    B. Rozemberczki, P. Scherer, Y . He, G. Panagopoulos, A. Riedel, M. Astefanoaei, O. Kiss, F. Beres, G. López, N. Collignon, and R. Sarkar, “Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models,” in Proceedings of the 30th ACM Interna...

    2021 doi

  6. [14]

    Geotorch: a spatiotemporal deep learning framework,

    K. Chowdhury and M. Sarwat, “Geotorch: a spatiotemporal deep learning framework,” ser. SIGSPATIAL ’22. New York, NY , USA: Association for Computing Machinery, 2022. [Online]. Available: https://doi.org/10.1145/3557915.3561036

    2022 doi

  7. [15]

    Deep learning with spatiotemporal data: A deep dive into geotor- chai,

    ——, “Deep learning with spatiotemporal data: A deep dive into geotor- chai,” in 2024 IEEE 40th International Conference on Data Engineering (ICDE), 2024, pp. 5156–5169

    2024

  8. [16]

    A demonstration of geotorchai: A spatiotemporal deep learning framework,

    ——, “A demonstration of geotorchai: A spatiotemporal deep learning framework,” in Companion of the 2023 International Conference on Management of Data, ser. SIGMOD ’23. Association for Computing Machinery, 2023, p. 195–198

    2023

  9. [17]

    A survey of nl2sql with large language models: Where are we, and where are we going?

    X. Liu, S. Shen, B. Li, P. Ma, R. Jiang, Y . Zhang, J. Fan, G. Li, N. Tang, and Y . Luo, “A survey of nl2sql with large language models: Where are we, and where are we going?” arXiv preprint arXiv:2408.05109, 2024

    2024

  10. [18]

    Rethinking data in nl2sql: a survey of what we have and what we expect,

    Y . Fan, Q. Weng, Y . Chen, and X. S. Wang, “Rethinking data in nl2sql: a survey of what we have and what we expect,” Vicinagearth, vol. 2, no. 1, p. 15, 2025

    2025

  11. [19]

    Form vs function: Database normalization’s effect on zero-shot nl2sql

    C. V oß, “Form vs function: Database normalization’s effect on zero-shot nl2sql.”

  12. [20]

    Poly2vec: Polymorphic fourier-based encoding of geospatial objects for geoai ap- plications,

    M. D. Siampou, J. Li, J. Krumm, C. Shahabi, and H. Lu, “Poly2vec: Polymorphic fourier-based encoding of geospatial objects for geoai ap- plications,” arXiv preprint arXiv:2408.14806, 2024

    2024

  13. [21]

    A natural language interface for crime-related spatial queries,

    C. Zhang, Y . Huang, R. Mihalcea, and H. Cuellar, “A natural language interface for crime-related spatial queries,” in 2009 IEEE International Conference on Intelligence and Security Informatics. IEEE, 2009, pp. 164–166

    2009

  14. [22]

    A grammar for interpret- ing geo-analytical questions as concept transformations,

    H. Xu, E. Nyamsuren, S. Scheider, and E. Top, “A grammar for interpret- ing geo-analytical questions as concept transformations,” International Journal of Geographical Information Science, vol. 37, no. 2, pp. 276– 306, 2023

    2023

  15. [23]

    r3-nl2gql: A model coordination and knowledge graph alignment approach for nl2gql,

    Y . Zhou, Y . He, S. Tian, Y . Ni, Z. Yin, X. Liu, C. Ji, S. Liu, X. Qiu, G. Ye et al., “r3-nl2gql: A model coordination and knowledge graph alignment approach for nl2gql,” in Findings of the Association for Computational Linguistics: EMNLP 2024, 2024, pp. 13 679–13 692

    2024

  16. [24]

    Nat-nl2gql: A novel multi-agent framework for translating natural language to graph query language,

    Y . Liang, T. Xie, G. Peng, Z. Huang, Y . Lan, and W. Qian, “Nat-nl2gql: A novel multi-agent framework for translating natural language to graph query language,” arXiv preprint arXiv:2412.10434, 2024

    2024

  17. [25]

    Geo2vec: Shape-and distance-aware neural representation of geospatial entities,

    C. Chu and C. Shahabi, “Geo2vec: Shape-and distance-aware neural representation of geospatial entities,” arXiv preprint arXiv:2508.19305, 2025

    2025

  18. [26]

    Natural language interface for database: a brief review,

    N. Nihalani, S. Silakari, and M. Motwani, “Natural language interface for database: a brief review,” International Journal of Computer Science Issues (IJCSI), vol. 8, no. 2, p. 600, 2011

    2011

  19. [27]

    A review of different approaches in natural language interfaces to databases,

    E. Reshma and P. Remya, “A review of different approaches in natural language interfaces to databases,” in 2017 International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2017, pp. 801–804

    2017

  20. [28]

    Natural language interfaces for tabular data querying and visualization: A survey,

    W. Zhang, Y . Wang, Y . Song, V . J. Wei, Y . Tian, Y . Qi, J. H. Chan, R. C.- W. Wong, and H. Yang, “Natural language interfaces for tabular data querying and visualization: A survey,” IEEE transactions on knowledge and data engineering, vol. 36, no. 11, pp. 6699–6718, 2024

    2024

  21. [29]

    A systematic re- view of natural language interfaces for databases,

    M. LIU, X. W ANG, J. XU, W. YI, and O. WOLFSON, “A systematic re- view of natural language interfaces for databases,” Frontiers of Computer Science, vol. 20, no. 11, p. 2011623, 2025

    2025

  22. [30]

    A comparative survey of recent natural language interfaces for databases,

    K. Affolter, K. Stockinger, and A. Bernstein, “A comparative survey of recent natural language interfaces for databases,” The VLDB Journal, vol. 28, no. 5, pp. 793–819, 2019

    2019

  23. [31]

    Neural approaches for natural language interfaces to databases: A survey,

    R. C. A. Iacob, F. Brad, E.-S. Apostol, C.-O. Truic ˘a, I. A. Hosu, and T. Rebedea, “Neural approaches for natural language interfaces to databases: A survey,” in proceedings of the 28th International Conference on Computational Linguistics, 2020, pp. 381–395

    2020

  24. [32]

    A survey of natural language processing implementation for data query systems,

    A. Wong, D. Joiner, C. Chiu, M. Elsayed, K. Pereira, Y . Khmelevsky, and J. Mahony, “A survey of natural language processing implementation for data query systems,” in 2021 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE). IEEE, 2021, pp. 1–8

    2021

  25. [33]

    A review of datasets for nlidbs,

    A. Das and R. Balabantaray, “A review of datasets for nlidbs,” Informa- tion and Communication Technology for Competitive Strategies (ICTCS 2022), pp. 213–223, 2023

    2022

  26. [34]

    A survey on deep learn- ing approaches for text-to-sql,

    G. Katsogiannis-Meimarakis and G. Koutrika, “A survey on deep learn- ing approaches for text-to-sql,” The VLDB Journal, vol. 32, no. 4, pp. 905–936, 2023

    2023

  27. [35]

    Natural language to sql: State of the art and open problems,

    Y . Luo, G. Li, J. Fan, C. Chai, and N. Tang, “Natural language to sql: State of the art and open problems,” Proceedings of the VLDB Endowment, vol. 18, no. 12, pp. 5466–5471, 2025

    2025

  28. [36]

    Next-generation database interfaces: A survey of llm-based text-to-sql,

    Z. Hong, Z. Yuan, Q. Zhang, H. Chen, J. Dong, F. Huang, and X. Huang, “Next-generation database interfaces: A survey of llm-based text-to-sql,” IEEE Transactions on Knowledge and Data Engineering, 2025

    2025

  29. [37]

    Nli4db: A systematic review of natural language interfaces for databases,

    M. Liu and J. Xu, “Nli4db: A systematic review of natural language interfaces for databases,” arXiv preprint arXiv:2503.02435, 2025

    2025

  30. [38]

    A survey of text-to-sql in the era of llms: Where are we, and where are we going?

    X. Liu, S. Shen, B. Li, P. Ma, R. Jiang, Y . Zhang, J. Fan, G. Li, N. Tang, and Y . Luo, “A survey of text-to-sql in the era of llms: Where are we, and where are we going?” IEEE Transactions on Knowledge and Data Engineering, 2025

    2025

  31. [39]

    Llm/agent-as-data-analyst: A survey,

    Z. Tang, W. Wang, Z. Zhou, Y . Jiao, B. Xu, B. Niu, D. Zhou, X. Zhou, G. Li, Y . He et al., “Llm/agent-as-data-analyst: A survey,” arXiv preprint arXiv:2509.23988, 2025

    2025

  32. [40]

    A survey of large language model-based generative ai for text-to-sql: Benchmarks, applications, use cases, and challenges,

    A. Singh, A. Shetty, A. Ehtesham, S. Kumar, and T. T. Khoei, “A survey of large language model-based generative ai for text-to-sql: Benchmarks, applications, use cases, and challenges,” in 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, ...

    2025

  33. [41]

    A survey on employing large language models for text-to-sql tasks,

    L. Shi, Z. Tang, N. Zhang, X. Zhang, and Z. Yang, “A survey on employing large language models for text-to-sql tasks,” ACM Computing Surveys, vol. 58, no. 2, pp. 1–37, 2025

    2025

  34. [42]

    Natural language interfaces for tabular data querying and visualization: A survey,

    W. Zhang, Y . Wang, Y . Song, V . J. Wei, Y . Tian, Y . Qi, J. H. Chan, R. C.-W. Wong, and H. Yang, “Natural language interfaces for tabular data querying and visualization: A survey,” 2024

    2024

  35. [43]

    Large language model enhanced text- to-sql generation: A survey,

    X. Zhu, Q. Li, L. Cui, and Y . Liu, “Large language model enhanced text- to-sql generation: A survey,” 2024

    2024

  36. [44]

    A survey of text-to-sql in the era of llms: Where are we, and where are we going?

    X. Liu, S. Shen, B. Li, P. Ma, R. Jiang, Y . Zhang, J. Fan, G. Li, N. Tang, and Y . Luo, “A survey of text-to-sql in the era of llms: Where are we, and where are we going?” 2025

    2025

  37. [45]

    Next-generation database interfaces: A survey of llm-based text-to-sql,

    Z. Hong, Z. Yuan, Q. Zhang, H. Chen, J. Dong, F. Huang, and X. Huang, “Next-generation database interfaces: A survey of llm-based text-to-sql,” 2025

    2025

  38. [46]

    Large language models for time series: A survey,

    X. Zhang, R. R. Chowdhury, R. K. Gupta, and J. Shang, “Large language models for time series: A survey,” arXiv preprint arXiv:2402.01801, 2024

    2024

  39. [47]

    The role of open-source llms in shaping the future of geoai,

    X. Huang, Z. Tu, X. Ye, and M. Goodchild, “The role of open-source llms in shaping the future of geoai,” Annals of GIS, pp. 1–10, 2026

    2026

  40. [48]

    Large models for time series and spatio-temporal data: A survey and outlook,

    M. Jin, Q. Wen, Y . Liang, C. Zhang, S. Xue, X. Wang, J. Zhang, Y . Wang, H. Chen, X. Li et al., “Large models for time series and spatio-temporal data: A survey and outlook,” arXiv preprint arXiv:2310.10196, 2023

    2023

  41. [49]

    A survey on time-series pre-trained models,

    Q. Ma, Z. Liu, Z. Zheng, Z. Huang, S. Zhu, Z. Yu, and J. T. Kwok, “A survey on time-series pre-trained models,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 12, pp. 7536–7555, 2024

    2024

  42. [50]

    A comprehensive survey of agentic ai for spatio-temporal data,

    M. Hashemi and A. Züfle, “A comprehensive survey of agentic ai for spatio-temporal data,” 2026

    2026

  43. [51]

    Spatialhadoop: towards flexible and scalable spatial pro- cessing using mapreduce,

    A. Eldawy, “Spatialhadoop: towards flexible and scalable spatial pro- cessing using mapreduce,” in Proceedings of the 2014 SIGMOD PhD Symposium, ser. SIGMOD’14 PhD Symposium. New York, NY , USA: Association for Computing Machinery, 2014, p. 46–50

    2014

  44. [52]

    Sedonadb,

    The Apache Software Foundation, “Sedonadb,” https://sedona.apache.org/sedonadb, accessed: 2025-12-05

    2025

  45. [53]

    Nalspatial: An effective natural language transformation framework for queries over spatial data,

    M. Liu, X. Wang, J. Xu, and H. Lu, “Nalspatial: An effective natural language transformation framework for queries over spatial data,” in Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, 2023, pp. 1–4

    2023

  46. [54]

    Timescaledb : Sql made scalable for time-series data,

    “Timescaledb : Sql made scalable for time-series data,” 2017. [Online]. Available: https://api.semanticscholar.org/CorpusID:34446750 20 VOLUME 4, 2016

    2017

  47. [55]

    Suitability of influxdb database for iot applications,

    M. Nasar and M. Abu Kausar, “Suitability of influxdb database for iot applications,” International Journal of Innovative Technology and Exploring Engineering, 08 2019

    2019

  48. [56]

    Spatialnli: A spatial domain natural language interface to databases using spatial compre- hension,

    J. Li, W. Wang, W.-S. Ku, Y . Tian, and H. Wang, “Spatialnli: A spatial domain natural language interface to databases using spatial compre- hension,” in Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019, pp. 339–348

    2019

  49. [57]

    Geosql-eval: First evaluation of llms on postgis-based nl2geosql queries,

    S. Hou, H. Jiao, Z. Liu, L. Xie, G. Chen, S. Wu, X. Guan, and H. Wu, “Geosql-eval: First evaluation of llms on postgis-based nl2geosql queries,” arXiv preprint arXiv:2509.25264, 2025

    2025

  50. [58]

    From nl2sql to nl2geosql: Geosql-eval for automated evaluation of llms on postgis queries

    S. Houa, H. Jiaob, Z. Liua, L. Xiea, G. Chenb, S. Wua, X. Guan, and H. Wua, “From nl2sql to nl2geosql: Geosql-eval for automated evaluation of llms on postgis queries.”

  51. [59]

    Clasp: Learning concepts for time-series signals from natural language supervision,

    A. Ito, K. Dohi, and Y . Kawaguchi, “Clasp: Learning concepts for time-series signals from natural language supervision,” arXiv preprint arXiv:2411.08397, 2024

    2024

  52. [60]

    Retrieving time-series differences using natural language queries,

    K. Dohi, T. Nishida, H. Purohit, T. Endo, and Y . Kawaguchi, “Retrieving time-series differences using natural language queries,” arXiv preprint arXiv:2503.21378, 2025

    2025

  53. [61]

    Nalmobench: Towards bench- marking natural language interfaces for moving objects databases,

    X. Wang, W. Yi, M. Liu, and C. Zong, “Nalmobench: Towards bench- marking natural language interfaces for moving objects databases,” Pro- ceedings of the VLDB Endowment. ISSN, vol. 2150, p. 8097

  54. [62]

    Promassistant: Leveraging large language models for text-to- promql,

    C. Zhang, B. Zhang, D. Yang, X. Peng, M. Chen, S. Xie, G. Chen, W. Bi, and W. Li, “Promassistant: Leveraging large language models for text-to- promql,” arXiv preprint arXiv:2503.03114, 2025

    2025

  55. [63]

    Extending a natural language interface with geospatial queries,

    V . R. Chintaphally, K. Neumeier, J. McFarlane, J. Cothren, and C. W. Thompson, “Extending a natural language interface with geospatial queries,” IEEE Internet Computing, vol. 11, no. 6, pp. 82–85, 2007

    2007

  56. [64]

    Spatialhadoop: A mapreduce framework for spatial data,

    A. Eldawy and M. F. Mokbel, “Spatialhadoop: A mapreduce framework for spatial data,” in 2015 IEEE 31st international conference on Data Engineering. IEEE, 2015, pp. 1352–1363

    2015

  57. [65]

    Nalsd: A natural language interface for spatial databases,

    M. Liu, X. Wang, and J. Xu, “Nalsd: A natural language interface for spatial databases,” in Proceedings of the 18th International Symposium on Spatial and Temporal Data, 2023, pp. 175–179

    2023

  58. [66]

    Nalspatial: A natural lan- guage interface for spatial databases,

    M. Liu, X. Wang, J. Xu, H. Lu, and Y . Tong, “Nalspatial: A natural lan- guage interface for spatial databases,” IEEE Transactions on Knowledge and Data Engineering, 2025

    2025

  59. [67]

    Towards a barrier- free geoqa portal: Natural language interaction with geospatial data using multi-agent llms and semantic search,

    Y . Feng, P. Zhang, G. Xiao, L. Ding, and L. Meng, “Towards a barrier- free geoqa portal: Natural language interaction with geospatial data using multi-agent llms and semantic search,” arXiv preprint arXiv:2503.14251, 2025

    2025

  60. [68]

    From questions to queries: An ai-powered multi-agent framework for spatial text-to-sql,

    A. Khosravi Kazazi, Z. Li, M. Naser Lessani, and G. Cervone, “From questions to queries: An ai-powered multi-agent framework for spatial text-to-sql,” arXiv e-prints, pp. arXiv–2510, 2025

    2025

  61. [69]

    Monkuu: a llm-powered natural language interface for geospatial databases with dynamic schema mapping,

    C. Yu, Y . Yao, X. Zhang, G. Zhu, Y . Guo, X. Shao, M. Shibasaki, Z. Hu, L. Dai, Q. Guan et al., “Monkuu: a llm-powered natural language interface for geospatial databases with dynamic schema mapping,” Inter- national Journal of Geographical Information Science, pp. 1–22, 2025

    2025

  62. [70]

    Spatial-rag: Spatial retrieval augmented generation for real-world spatial reasoning questions,

    D. Yu, R. Bao, G. Mai, and L. Zhao, “Spatial-rag: Spatial retrieval augmented generation for real-world spatial reasoning questions,” arXiv preprint arXiv:2502.18470, 2025

    2025

  63. [71]

    Berlinmod: a benchmark for moving object databases,

    C. Düntgen, T. Behr, and R. H. Güting, “Berlinmod: a benchmark for moving object databases,” The VLDB Journal, vol. 18, no. 6, pp. 1335– 1368, 2009

    2009

  64. [72]

    Nlmo: towards a natural language tool for querying moving objects,

    X. Wang, J. Xu, and Y . Wang, “Nlmo: towards a natural language tool for querying moving objects,” in 2020 21st IEEE International Conference on Mobile Data Management (MDM). IEEE, 2020, pp. 228–229

    2020

  65. [73]

    Nalmo: A natural language interface for moving objects databases,

    X. Wang, J. Xu, and H. Lu, “Nalmo: A natural language interface for moving objects databases,” in Proceedings of the 17th International Symposium on Spatial and Temporal Databases, 2021, pp. 1–11

    2021

  66. [74]

    Nalmo: Transforming queries in natural language for moving objects databases,

    X. Wang, M. Liu, J. Xu, and H. Lu, “Nalmo: Transforming queries in natural language for moving objects databases,” GeoInformatica, vol. 27, no. 3, pp. 427–460, 2023

    2023

  67. [75]

    Querying moving objects in secondo,

    V . Teixeira de Almeida, R. Hartmut Guting, and T. Behr, “Querying moving objects in secondo,” in 7th International Conference on Mobile Data Management (MDM’06), 2006, pp. 47–47

    2006

  68. [76]

    R. H. Güting and M. Schneider, Moving objects databases. Academic Press, 2005

    2005

  69. [77]

    Introduction to postgis,

    P. Ramsey and V .-B. Columbia, “Introduction to postgis,” Refractions Research Inc, pp. 34–35, 2005

    2005

  70. [78]

    Text-to-overpassql: A natural language interface for complex geodata querying of open- streetmap,

    M. Staniek, R. Schumann, M. Züfle, and S. Riezler, “Text-to-overpassql: A natural language interface for complex geodata querying of open- streetmap,” Transactions of the Association for Computational Linguis- tics, vol. 12, p. 562–575, 2024

    2024

  71. [79]

    Bennett, OpenStreetMap

    J. Bennett, OpenStreetMap. Packt Publishing Ltd, 2010

    2010

  72. [80]

    Overpass api,

    R. Olbricht et al., “Overpass api,” Anwenderkonferenz für Freie und Open Source Software für Geoinformationssysteme, 2011

    2011

  73. [81]

    Oracle spatial developer’s guide, 11g release 1 (11.1) b28400-05

    D. Abugov, N. Alexander, B. Blackwell, R. Chatterjee, D. Geringer, M. Horhammer, Y . Hu, B. Kazar, R. Kothuri, S. Ravada et al., “Oracle spatial developer’s guide, 11g release 1 (11.1) b28400-05.”

  74. [82]

    Aquifer characterisation using surface nmr jointly with other geophysical techniques at the nauen/berlin test site,

    U. Yaramanci, G. Lange, and M. Hertrich, “Aquifer characterisation using surface nmr jointly with other geophysical techniques at the nauen/berlin test site,” Journal of Applied Geophysics, vol. 50, no. 1-2, pp. 47–65, 2002

    2002

  75. [83]

    Extracting interrogative intents and concepts from geo- analytic questions,

    H. Xu, E. Hamzei, E. Nyamsuren, H. Kruiger, S. Winter, M. Tomko, and S. Scheider, “Extracting interrogative intents and concepts from geo- analytic questions,” AGILE: GIScience Series, vol. 1, p. 23, 2020

    2020

  76. [84]

    Learning to parse natural language database queries into logical form,

    C. A. Thompson, R. J. Mooney, and L. R. Tang, “Learning to parse natural language database queries into logical form,” in Workshop on Automata Induction, Grammatical Inference and Language Acquisition, 1997

    1997

  77. [85]

    Learning dependency-based compositional semantics,

    P. Liang, M. I. Jordan, and D. Klein, “Learning dependency-based compositional semantics,” Computational Linguistics, vol. 39, no. 2, pp. 389–446, 2013

    2013

  78. [86]

    Truth-conditional captioning of time series data,

    H. Jhamtani and T. Berg-Kirkpatrick, “Truth-conditional captioning of time series data,” in EMNLP, 2021

    2021

  79. [87]

    Sushi: A dataset of synthetic unichannel signals based on heuristic implementation (tiny),

    Y . Kawaguchi, K. Dohi, and A. Ito, “Sushi: A dataset of synthetic unichannel signals based on heuristic implementation (tiny),” Sep. 2024. [Online]. Available: https://doi.org/10.5281/zenodo.13882998

    2024 doi

  80. [88]

    Domain-independent automatic generation of descriptive texts for time- series data,

    K. Dohi, A. Ito, H. Purohit, T. Nishida, T. Endo, and Y . Kawaguchi, “Domain-independent automatic generation of descriptive texts for time- series data,” 09 2024

    2024

  81. [89]

    Kaggledbqa: Realistic evaluation of text-to-sql parsers,

    C. Lee, O. Polozov, and M. Richardson, “Kaggledbqa: Realistic evaluation of text-to-sql parsers,” CoRR, vol. abs/2106.11455, 2021. [Online]. Available: https://arxiv.org/abs/2106.11455

    2021

  82. [90]

    Spatialite: An open source spatial database engine,

    A. Furieri, “Spatialite: An open source spatial database engine,” 2011

    2011

  83. [91]

    Natural language interfaces for structured query generation in iod platforms,

    A. Sezgin, “Natural language interfaces for structured query generation in iod platforms,” Drones, vol. 9, no. 6, 2025. [Online]. Available: https://www.mdpi.com/2504-446X/9/6/444

    2025

  84. [92]

    A fuzzy grammar and possibility theory-based natural lan- guage user interface for spatial queries,

    F. Wang, “A fuzzy grammar and possibility theory-based natural lan- guage user interface for spatial queries,” Fuzzy sets and systems, vol. 113, no. 1, pp. 147–159, 2000

    2000

  85. [93]

    From queries to insights: Agentic llm pipelines for spatio-temporal text-to-sql,

    M. Redd, T. Zhe, and D. Wang, “From queries to insights: Agentic llm pipelines for spatio-temporal text-to-sql,” in Proceedings of the 1st ACM SIGSPATIAL International Workshop on Generative and Agentic AI for Multi-Modality Space-Time Intelligence, 2025, pp. 6–14

    2025

  86. [94]

    Natural language processing,

    K. Chowdhary, “Natural language processing,” Fundamentals of artificial intelligence, pp. 603–649, 2020

    2020

  87. [95]

    Named entity extraction for knowledge graphs: A literature overview,

    T. Al-Moslmi, M. G. Ocaña, A. L. Opdahl, and C. Veres, “Named entity extraction for knowledge graphs: A literature overview,” IEEE access, vol. 8, pp. 32 862–32 881, 2020

    2020

  88. [96]

    Natural language processing (nlp) in disease detection—a discussion of how nlp techniques can be used to analyze and classify medical text data for disease diagnosis,

    V . Kumar, M. I. Iqbal, and R. Rathore, “Natural language processing (nlp) in disease detection—a discussion of how nlp techniques can be used to analyze and classify medical text data for disease diagnosis,” AI in Disease Detection: Advancements and Applications, pp. 53–75, 2025

    2025

  89. [97]

    Progress in natural language understanding: an applica- tion to lunar geology,

    W. A. Woods, “Progress in natural language understanding: an applica- tion to lunar geology,” in Proceedings of the June 4-8, 1973, national computer conference and exposition, 1973, pp. 441–450

    1973

  90. [98]

    Towards a theory of natural language interfaces to databases,

    A.-M. Popescu, O. Etzioni, and H. Kautz, “Towards a theory of natural language interfaces to databases,” in Proceedings of the 8th international conference on Intelligent user interfaces, 2003, pp. 149–157

    2003

  91. [99]

    Nalir: an interactive natural language interface for querying relational databases,

    F. Li and H. V . Jagadish, “Nalir: an interactive natural language interface for querying relational databases,” in Proceedings of the 2014 ACM SIGMOD international conference on Management of data, 2014, pp. 709–712

    2014

  92. [100]

    Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars,

    L. S. Zettlemoyer and M. Collins, “Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars,” arXiv preprint arXiv:1207.1420, 2012

    2012 arXiv

  93. [101]

    Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers,

    B. Wang, R. Shin, X. Liu, O. Polozov, and M. Richardson, “Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers,” in Proceedings of the 58th annual meeting of the association for computa- tional linguistics, 2020, pp. 7567–7578

    2020

  94. [102]

    Picard: Parsing incremen- tally for constrained auto-regressive decoding from language models,

    T. Scholak, N. Schucher, and D. Bahdanau, “Picard: Parsing incremen- tally for constrained auto-regressive decoding from language models,” arXiv preprint arXiv:2109.05093, 2021. VOLUME 4, 2016 21

    2021

  95. [103]

    Is gpt-3 a good data annotator?

    B. Ding, C. Qin, L. Liu, Y . K. Chia, B. Li, S. Joty, and L. Bing, “Is gpt-3 a good data annotator?” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (V olume 1: Long Papers), 2023, pp. 11 173–11 195

    2023

  96. [104]

    Din-sql: Decomposed in-context learning of text-to-sql with self-correction,

    M. Pourreza and D. Rafiei, “Din-sql: Decomposed in-context learning of text-to-sql with self-correction,” Advances in Neural Information Processing Systems, vol. 36, pp. 36 339–36 348, 2023

    2023

  97. [105]

    Resdsql: Decoupling schema linking and skeleton parsing for text-to-sql,

    H. Li, J. Zhang, C. Li, and H. Chen, “Resdsql: Decoupling schema linking and skeleton parsing for text-to-sql,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 11, 2023, pp. 13 067– 13 075

    2023

  98. [106]

    Sql-palm: Improved large language model adaptation for text-to-sql (extended),

    R. Sun, S. Ö. Arik, A. Muzio, L. Miculicich, S. Gundabathula, P. Yin, H. Dai, H. Nakhost, R. Sinha, Z. Wang et al., “Sql-palm: Improved large language model adaptation for text-to-sql (extended),” arXiv preprint arXiv:2306.00739, 2023

    2023

  99. [107]

    An approach for solving of natural language queries and transliteration using multi-agent system

    N. M. Shelke, R. Dharaskar, and V . Thakre, “An approach for solving of natural language queries and transliteration using multi-agent system.”

  100. [108]

    Mac-sql: A multi-agent collaborative framework for text-to-sql,

    B. Wang, C. Ren, J. Yang, X. Liang, J. Bai, L. Chai, Z. Yan, Q.-W. Zhang, D. Yin, X. Sun et al., “Mac-sql: A multi-agent collaborative framework for text-to-sql,” in Proceedings of the 31st International Conference on Computational Linguistics, 2025, pp. 540–557

    2025

  101. [109]

    Agentiql: An agent-inspired multi-expert framework for text-to-sql generation,

    O. R. Heidari, S. Reid, and Y . Yaakoubi, “Agentiql: An agent-inspired multi-expert framework for text-to-sql generation,” arXiv preprint arXiv:2510.10661, 2025

    2025

  102. [110]

    Grammar- based neural text-to-sql generation,

    K. Lin, B. Bogin, M. Neumann, J. Berant, and M. Gardner, “Grammar- based neural text-to-sql generation,” arXiv preprint arXiv:1905.13326, 2019

    1905 arXiv

  103. [111]

    Reforce: A text-to-sql agent with self-refinement, format restriction, and column exploration,

    M. Deng, A. Ramachandran, C. Xu, L. Hu, Z. Yao, A. Datta, and H. Zhang, “Reforce: A text-to-sql agent with self-refinement, format restriction, and column exploration,” in ICLR 2025 Workshop: VerifAI: AI Verification in the Wild, 2025

    2025

  104. [112]

    Optimizing reasoning for text- to-sql with execution feedback,

    B. Zhai, C. Xu, Y . He, and Z. Yao, “Optimizing reasoning for text- to-sql with execution feedback,” in Findings of the Association for Computational Linguistics: ACL 2025, 2025, pp. 19 206–19 218

    2025

  105. [113]

    Sqlgenie: A practical llm based system for reliable and efficient sql generation,

    P. Ghosh, A. Jain, and P. Yenigalla, “Sqlgenie: A practical llm based system for reliable and efficient sql generation,” 2025

    2025

  106. [114]

    Engineering of generative artificial intelligence and natural language processing models to accurately identify arrhythmia recurrence,

    R. Feng, K. A. Brennan, Z. Azizi, J. Goyal, B. Deb, H. J. Chang, P. Gane- san, P. Clopton, M. Pedron, S. Ruiperez-Campillo et al., “Engineering of generative artificial intelligence and natural language processing models to accurately identify arrhythmia recurrence,” Circulati...

    2025

  107. [115]

    Natural language to sql: A semantic mapping and metadata approach for database interaction,

    J. Patel, “Natural language to sql: A semantic mapping and metadata approach for database interaction,” in International Conference on Com- puter Science and Communication Engineering (ICCSCE 2025). At- lantis Press, 2025, pp. 1405–1418

    2025

  108. [116]

    Classifying the state of knowledge-based question answering: patterns, progress, and prospects,

    S. Sharma, S. Jain, M. K. Tiwari, and S. lal, “Classifying the state of knowledge-based question answering: patterns, progress, and prospects,” International Journal of Computers and Applications, vol. 47, no. 1, pp. 93–105, 2025

    2025

  109. [117]

    Generative alignment and semantic parsing for learning from ambiguous supervision,

    J. Kim and R. Mooney, “Generative alignment and semantic parsing for learning from ambiguous supervision,” in Coling 2010: Posters, 2010, pp. 543–551

    2010

  110. [118]

    Learning language semantics from am- biguous supervision,

    R. J. Kate and R. J. Mooney, “Learning language semantics from am- biguous supervision,” in AAAI, vol. 7, 2007, pp. 895–900

    2007

  111. [119]

    Text-to-sql in the wild: A naturally-occurring dataset based on stack exchange data,

    M. Hazoom, V . Malik, and B. Bogin, “Text-to-sql in the wild: A naturally-occurring dataset based on stack exchange data,” arXiv preprint arXiv:2106.05006, 2021

    2021

  112. [120]

    Combining small language models and large language models for zero-shot nl2sql,

    J. Fan, Z. Gu, S. Zhang, Y . Zhang, Z. Chen, L. Cao, G. Li, S. Madden, X. Du, and N. Tang, “Combining small language models and large language models for zero-shot nl2sql,” Proceedings of the VLDB En- dowment, vol. 17, no. 11, pp. 2750–2763, 2024

    2024

  113. [121]

    Multilingual text-to-sql: Benchmarking the limits of language models with collaborative language agents,

    K. T. Pham, T. H. Nguyen, J. Jo, Q. V . H. Nguyen, and T. T. Nguyen, “Multilingual text-to-sql: Benchmarking the limits of language models with collaborative language agents,” arXiv preprint arXiv:2509.24405, 2025

    2025

  114. [122]

    Towards advanced natural language interfaces for moving objects databases,

    X. Wang and J. Xu, “Towards advanced natural language interfaces for moving objects databases,” in Proceedings of the 19th International Symposium on Spatial and Temporal Data, 2025, pp. 111–114

    2025

  115. [123]

    Geo-hallucination in urban analytics: What it is and why it matters,

    X. Huang, “Geo-hallucination in urban analytics: What it is and why it matters,” Environment and Planning B: Urban Analytics and City Science, p. 23998083251409526, 2025

    2025

  116. [124]

    Towards llms robustness to changes in prompt format styles,

    L. Ngweta, K. Kate, J. Tsay, and Y . Rizk, “Towards llms robustness to changes in prompt format styles,” in Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Compu- tational Linguistics: Human Language Technologies (V olume 4: Stu...

    2025

  117. [125]

    Safenlidb: A privacy-preserving safety alignment framework for llm-based natural language database interfaces,

    R. Liu, X. Chen, J. Zhang, Q. Zhang, Y . Zhang, and B. Yang, “Safenlidb: A privacy-preserving safety alignment framework for llm-based natural language database interfaces,” arXiv preprint arXiv:2511.06778, 2025

    2025

  118. [126]

    Conversational interfaces for data analysis: Evaluating mod- ular agent architectures,

    M. Rauch, “Conversational interfaces for data analysis: Evaluating mod- ular agent architectures,” 2025

    2025

  119. [127]

    Sql-factory: A multi- agent framework for high-quality and large-scale sql generation,

    J. Li, T. Wu, Y . Mao, Y . Gao, Y . Feng, and H. Liu, “Sql-factory: A multi- agent framework for high-quality and large-scale sql generation,” arXiv preprint arXiv:2504.14837, 2025

    2025

  120. [128]

    A comprehensive exploration on wikisql with table-aware word contextualization,

    W. Hwang, J. Yim, S. Park, and M. Seo, “A comprehensive exploration on wikisql with table-aware word contextualization,” arXiv preprint arXiv:1902.01069, 2019

    1902

  121. [129]

    Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task,

    T. Yu, R. Zhang, K. Yang, M. Yasunaga, D. Wang, Z. Li, J. Ma, I. Li, Q. Yao, S. Roman et al., “Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task,” arXiv preprint arXiv:1809.08887, 2018

    2018 arXiv

  122. [130]

    Know what i don’t know: Handling ambiguous and unknown questions for text-to-sql,

    B. Wang, Y . Gao, Z. Li, and J.-G. Lou, “Know what i don’t know: Handling ambiguous and unknown questions for text-to-sql,” in Findings of the Association for Computational Linguistics: ACL 2023, 2023, pp. 5701–5714

    2023

  123. [131]

    I need help! evaluating llm’s ability to ask for users’ support: A case study on text-to-sql generation,

    C.-K. Wu, Z. R. Tam, C.-C. Wu, C.-Y . Lin, H.-y. Lee, and Y .-N. Chen, “I need help! evaluating llm’s ability to ask for users’ support: A case study on text-to-sql generation,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024, pp...

    2024

  124. [132]

    Did you ask a good question? a cross-domain question intention classification benchmark for text-to-sql,

    Y . Zhang, X. Dong, S. Chang, T. Yu, P. Shi, and R. Zhang, “Did you ask a good question? a cross-domain question intention classification benchmark for text-to-sql,” arXiv preprint arXiv:2010.12634, 2020

    2010

  125. [133]

    Characterising and categorising anonymization techniques: A literature-based approach

    A. Fieschi, P. Hirmer, C. Stach, and B. Mitschang, “Characterising and categorising anonymization techniques: A literature-based approach.” in ICISSP (1), 2025, pp. 107–118

    2025

  126. [134]

    A systematic overview on methods to protect sensitive data provided for various analyses,

    M. Templ and M. Sariyar, “A systematic overview on methods to protect sensitive data provided for various analyses,” International Journal of Information Security, vol. 21, no. 6, pp. 1233–1246, 2022

    2022

  127. [135]

    Towards mitigating llm hallucination via self reflection,

    Z. Ji, T. Yu, Y . Xu, N. Lee, E. Ishii, and P. Fung, “Towards mitigating llm hallucination via self reflection,” in Findings of the Association for Computational Linguistics: EMNLP 2023, 2023, pp. 1827–1843

    2023

  128. [136]

    Natural language to sql using large language models,

    G. Zhan, G. Shi, X. Liu, and N. Wang, “Natural language to sql using large language models,” in 2025 21st International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2025, pp. 495–502

    2025

  129. [137]

    Text-to-sql con- version by using deep learning/machine learning: Integrating natural language with database queries

    A. Kaygude, O. Rajguru, S. Karad, and G. Avhad, “Text-to-sql con- version by using deep learning/machine learning: Integrating natural language with database queries.”

  130. [138]

    Graph-fusion resdsql: Enhancing nl2sql robustness with dynamic schema linking and contextual integration,

    Y . Liu and T. Zhang, “Graph-fusion resdsql: Enhancing nl2sql robustness with dynamic schema linking and contextual integration,” in 2025 IEEE 7th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2025, pp. 221–225

    2025

  131. [139]

    Sql-o1: A self-reward heuristic dynamic search method for text- to-sql,

    S. Lyu, H. Luo, R. Li, Z. Ou, J. Sun, Y . Qin, X. Shang, M. Song, and Y . Zhu, “Sql-o1: A self-reward heuristic dynamic search method for text- to-sql,” arXiv preprint arXiv:2502.11741, 2025

    2025

  132. [140]

    Enhancing interaction graph of data schema and syntactic structure,

    W. Zhao, F. Wu, Z. Zheng1, and H. Jin1, “Enhancing interaction graph of data schema and syntactic structure,” in Big Data: 12th CCF Conference, BigData 2024, Qingdao, China, August 9–11, 2024, Proceedings, vol

    2024

  133. [141]

    Springer Nature, 2025, p. 159

    2025

  134. [142]

    Detriever: Decoder-representation-based retriever for improving nl2sql in-context learning,

    R. Li, Y . Feng, Z. Fan, G. Carenini, W. Zhang, M. Pourreza, and Y . Zhang, “Detriever: Decoder-representation-based retriever for improving nl2sql in-context learning,” in Proceedings of the 31st International Conference on Computational Linguistics, 2025, pp. 8173–8183

    2025

  135. [143]

    Sqlspace: A representation space for text-to-sql to discover and mitigate robustness gaps,

    N. Srikanth, V . Bursztyn, P. Mathur, and A. Nenkova, “Sqlspace: A representation space for text-to-sql to discover and mitigate robustness gaps,” arXiv preprint arXiv:2510.27532, 2025. 22 VOLUME 4, 2016

    2025

Pith tools

Reviewed May 15, 2026 · model on record in the stance chip above.