Pith. sign in

REVIEW 4 major objections 6 minor 51 references

MDB-Link: Hierarchical Schema Linking for Multi-Database Text-to-SQL

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that multi-database schema linking should be decomposed into three narrowing stages—database localization, table selection, and column grounding—and that a global column-level vector index plus a budget-aware LLM reranker…

desk verdict A solid, clearly-written system paper with a genuinely new hierarchical pipeline, but the headline gains over LinkAlign are not fully attributable to the design until the baseline's retrieval setup is matched. read the letter →

arxiv 2608.09588 v1 pith:TV3KT2ID submitted 2026-08-10 cs.CL

Beiyu Xu , Zhenyu Wu , Jiaoyan Chen , Riza theresa Batista-navarro This is my paper · ORCID
classification cs.CL
keywords multi-databasetext-to-SQLschemalinkingdatabaselocalizationcolumnretrievalLLMrerankingbudget-awarepromptinghierarchicalreductionvectorindex
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

Text-to-SQL systems usually assume the target database is known. This paper studies the harder, realistic setting where a natural-language question must be routed to the right database inside a large heterogeneous collection, and then to the right tables and columns. It proposes MDB-Link, which builds a global vector index over every column, retrieves question-relevant columns, aggregates that evidence to shortlist databases, reranks them with an LLM, then selects tables and grounds columns under a token budget. On three multi-database benchmarks, the method lifts exact schema match from 16.88 to 51.41 on MMQA, 2.50 to 9.17 on Spider2-Snow, and 12.52 to 38.01 on BIRD-dev with Qwen2.5-14B, while improving database localization in every comparison and running faster than the main baselines. The paper argues that schema linking should be treated as a hierarchical reduction problem rather than flat retrieval or unconstrained schema expansion.

What carries the argument

The load-bearing mechanism is the global column-wise vector index plus the two-round database localization loop. Each column of every candidate database is serialized into a retrieval document (table name, column name, descriptions, sample values, key metadata), embedded, and stored with provenance; at inference, the question is embedded once and used to retrieve top columns in two rounds: first across the whole collection with a support-based pruning rule that keeps databases with enough hits or a highly confident column, then, if too many candidates remain, within the top-κ after LLM reranking. The LLM reranker converts the next-token probability of 'yes' into a relevance score, and the same full-schema-first renderer is shared across reranking and table selection, truncating by cosine similarity while protecting primary and foreign keys. This design makes the schema context a function of the complete prompt budget, including template and question, rather than a fixed context window.

What would settle it

Remove or corrupt a meaningful fraction (say 30%) of column descriptions and sample values in the BIRD-dev or MMQA metadata, rebuild the index and prompts, and re-run localization and grounding; if exact match and localization accuracy fall far outside the ranges reported in Table 2, the claimed improvements are contingent on clean metadata rather than on the hierarchical reduction itself.

Watch

Extended reading notes

Core claim

The central claim is that multi-database schema linking is best solved by decomposing it into three interdependent reductions—database localization, table selection, and column-wise grounding—and that column-level evidence from a global vector index can drive all three. MDB-Link first retrieves the top columns for the question across the whole collection, converts hit counts and maximum similarities into a database shortlist, and uses a budget-aware LLM reranker that scores each candidate by the normalized probability of a 'yes' token to pick the target. Within the chosen database, a full-schema-first renderer includes all tables and columns when the prompt budget allows, and otherwise keeps the highest-similarity column per table plus primary and foreign keys. The result is a schema subset close in size to the gold schema, and on the three benchmarks the framework improves localization accuracy in all six backbone–dataset comparisons, improves exact match in five of six, and improves downstream execution accuracy over LinkAlign in all six while consuming less time than LinkAlign and AutoLink.

Load-bearing premise

The benchmarks provide complete, clean, static schema metadata—column names, descriptions, sample values, and primary and foreign keys—and the method's retrieval index and prompt renderer depend on that metadata; the paper's Limitations section concedes that robustness to noisy, incomplete, or outdated metadata has not been evaluated.

Editorial extensions

If this is right

  • Database localization can be driven by column-level retrieval evidence alone, without scanning whole schemas, making the approach feasible for collections with hundreds of thousands of columns (Spider2-Snow has roughly 102k).
  • Prompt-budget-aware schema rendering—full schema first, then similarity-ranked truncation that keeps key columns—lets one method serve both small and very large schemas without per-dataset tuning.
  • Compact linked schemas close to gold size are sufficient for downstream SQL generation: MDB-Link matches or beats AutoLink's much larger schemas on execution accuracy in most comparisons while using far fewer tokens.
  • The hierarchical order means localization errors are catastrophic and unrecoverable, so improving database localization accuracy is the highest-leverage component of the pipeline.

Reading between the lines

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

  • The design implies that the method's ceiling is set by localization: because table selection and column grounding operate inside the predicted database, any database misprediction invalidates every downstream choice, so a natural stress test is a collection of deliberately confusable databases with overlapping names and domains.
  • The reranker's yes/no logit score is a general calibration trick that converts an LLM into a relevance filter using constrained decoding, and it could be lifted into any retrieval pipeline, not just text-to-SQL.
  • The budget-aware renderer's priority order (highest-similarity column per table, then keys, then fill by similarity) defines an implicit schema salience ranking that could be reused for prompt compression in other structured tasks such as data-frame question answering or API selection.
  • An untested consequence is that nothing in the design is SQL-specific, so the same hierarchical localization–selection–grounding loop should apply to question answering over document collections or knowledge graphs that require a routing step.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper studies multi-database text-to-SQL, where the target database is not given and must be localized before schema linking. It proposes MDB-Link, a hierarchical pipeline that builds a global column-wise vector index over all databases, retrieves question-relevant columns, aggregates this evidence to shortlist candidate databases, applies LLM-based reranking on a budget-aware rendered schema, and then performs table selection and column-wise grounding before passing a compact schema to a downstream SQL generator. The method is evaluated on MMQA, Spider2-Snow, and BIRD-dev against LinkAlign, AutoLink, Single-Prompt, and Dense Retrieval, using schema-level metrics (LA, EM, Recall, #Cols, token/time cost) and downstream execution accuracy with a fixed Spider-Agent. The headline results under Qwen2.5-14B show large EM gains over LinkAlign (e.g., 16.88 to 51.41 on MMQA and 2.50 to 9.17 on Spider2-Snow), lower runtime than LinkAlign and AutoLink, and downstream EX improvements over LinkAlign on all datasets.

Significance. If the reported gains hold under a properly matched evaluation, MDB-Link is a meaningful contribution to an under-studied and practically important setting: routing a natural-language query to the correct database in a large, heterogeneous collection and producing a compact, SQL-relevant schema. The evaluation protocol is thoughtfully multi-dimensional, jointly reporting localization accuracy, exact schema match, recall, compactness, token cost, runtime, and downstream execution accuracy, and the ablations in Appendix H are a useful breakdown of the individual modules. The paper is also candid in its limitations section about the reliance on complete, clean schema metadata and the lack of systematic robustness evaluation. The main risks are baseline comparability and statistical reliability: several headline numbers come from very small samples, there are no confidence intervals or significance tests, and the LinkAlign baseline's retrieval instantiation is not specified, which makes the attribution of the observed gains to the hierarchical design less certain.

major comments (4)
  1. [§5.2, §5.4, Table 2] The LinkAlign baseline is not sufficiently specified to support the causal attribution in the abstract. Section 5.4 states that MDB-Link uses Qwen3-Embedding-0.6B for its column index and gives the hyperparameters in Appendix D, but it does not state which embedding model LinkAlign uses, how its retrieval or alignment is implemented, or how its schema context is budgeted. If LinkAlign runs with a weaker or differently configured retriever, the reported LA/EM/Recall gains could be due to the embedding index or schema-rendering budget rather than the hierarchical localization-first design. Please report LinkAlign's retrieval configuration, or rerun LinkAlign with the same embedding index and budget settings, and verify that the LLM backbones used for LinkAlign's own alignment steps match those used for MDB-Link's LLM modules.
  2. [§6.1, Table 2, Appendix H] The empirical claims are presented without error bars or significance tests, and several are based on very small sample counts. Spider2-Snow has only 120 examples, so the EM difference between LinkAlign (5.00) and MDB-Link (1.67) under Ministral-3-14B corresponds to about 6 versus 2 samples, and the M3 ablation in Table 10 moves EM from 1.67 to 3.33 when the reranker is removed. Please report at least binomial confidence intervals or a paired significance test for the main comparisons, and avoid wording such as 'consistent improvements' for differences that are within the expected noise range.
  3. [§7, Table 10] The ablation discussion is partially undercut by two exceptions that are acknowledged in Appendix H but not fully reconciled with the main-text interpretation. Removing database reranking improves EM on Spider2-Snow with Ministral-3-14B (1.67 to 3.33), and removing table selection improves Recall on BIRD-dev with Ministral-3-14B (82.72 to 86.29). Given the small sample sizes, these reversals may be noise, but the current text concludes that the two modules play 'complementary roles' without establishing that these reversals are not within measurement error. Please add uncertainty estimates to the ablation table and discuss whether the exceptions are statistically meaningful.
  4. [Appendix D, Section 4.2] The pipeline has several free hyperparameters (alpha, beta1, beta2, eta, rho, mu, kappa, and the complete-prompt cap), and all of them are fixed without a sensitivity analysis. Since the evaluation protocol and these values are authored in the same paper, a reader cannot tell whether the reported advantages are robust to reasonable changes in these settings. Please include a sensitivity study over at least the two or three most consequential parameters (e.g., alpha, mu, and the retrieval cap beta1), or provide a clear rationale based on validation data for the chosen values.
minor comments (6)
  1. [§8] The sentence 'MDB-Link improves LA and downstream EX over LinkAlign in all six datasets: backbone comparisons' appears to contain a typo; it should read 'dataset–backbone comparisons'.
  2. [§5.3] The definition of Exact Match should clarify whether the predicted database and table names are part of the exact match, and how a predicted schema with the same gold columns but a different table grouping is scored.
  3. [§5.4 and Appendix D] Please specify the decoding settings (temperature, sampling, or greedy decoding) for the LLM-based reranking, table selection, and column grounding modules; currently only the Spider-Agent temperature is reported.
  4. [Table 2] Because AutoLink's LA and EM are not reported, the abstract's statement about 'database localization and column selection' is only relative to LinkAlign. This should be stated explicitly in the introduction or the abstract to avoid implying a comparison with AutoLink on those metrics.
  5. [Limitations] There is a typo in the Limitations section: 'databalse' should be 'databases'.
  6. [Figure 1] In the database-aggregation example, 'Event_data' appears to be a typo for 'Event_date'; please check the figure for consistency with the example question and schema.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MDB-Link's predictions are produced by an independent pipeline and checked against external gold labels; no claimed result reduces by construction to its inputs.

full rationale

After walking the derivation chain, I find no step in which a predicted quantity is defined from, or fitted to, the target it is used to predict. The target database is produced by Algorithm 1 (column retrieval, support pruning, and LLM reranking), and the linked schema is produced by the table-selection and column-grounding prompts of Sections 4.3-4.4. These outputs are then compared against external gold labels via LA, EM, Recall, and EX; none of the equations, including the reranker score in Section 4.2, contains the gold database or gold column set as an input. The fixed hyperparameters in Appendix D are stated as fixed across datasets, and the paper does not describe any selection-on-evaluation-splits procedure, so the fitted-input-called-prediction pattern is not evidenced. The baselines and components (LinkAlign, AutoLink, Qwen models, Spider-Agent) are external works, and no load-bearing argument rests on a self-citation or on a uniqueness theorem imported from the authors. The ablations hold the embedding index fixed and remove the reranker and table-selection stages, providing partial independent support for the hierarchical-design claim. The skeptic's concern that LinkAlign's retrieval instantiation is unspecified is a fair experimental-control concern, but it is not a by-construction equivalence and therefore does not constitute circularity under the stated rules. The Limitations passage about noisy metadata is an acknowledged robustness gap, not a circular step. Overall, the paper is an empirical system paper with no equation-level or self-citation circularity.

Assumptions & free parameters 8 free parameters · 4 assumptions · 0 invented entities

The reported gains rest on a set of hand-chosen hyperparameters and on the availability of clean, complete schema metadata. No new theoretical entities are introduced. If the hyperparameters were tuned on the evaluation splits, the headline improvements would be partially in-sample; the paper does not report tuning.

free parameters (8)
  • alpha (HRC ratio) = 0.1
    Retrieval budget as a fraction of total columns; fixed in Appendix D without a tuning analysis.
  • beta1 (first-round retrieval cap) = 500
    Maximum number of columns retrieved in round 1; hand-chosen.
  • beta2 (second-round retrieval cap) = 50
    Maximum number of columns retrieved in round 2; hand-chosen.
  • eta (minimum hit count) = 2
    Support-based pruning threshold in PruneDB; hand-chosen.
  • rho (similarity quantile) = 0.8
    Quantile used to retain databases with one highly confident column; hand-chosen.
  • mu (database cap) = 10
    Maximum number of databases returned by PruneDB; hand-chosen.
  • kappa (second-round trigger) = 3
    Threshold for triggering the second retrieval round and the top-kappa reranker scope; hand-chosen.
  • complete-prompt cap = min(0.85L, L-512) with L=110,000
    Token budget for the complete prompt in budget-aware schema context construction; hand-chosen.
assumptions (4)
  • domain assumption Each question is assumed to belong to exactly one target database.
    MDB-Link outputs a single predicted database, and the evaluation protocol computes LA against one gold database. Cross-database queries are not addressed.
  • domain assumption Schema metadata used by the index and prompts is available, complete, and static.
    The paper's Limitations section states reliance on names, descriptions, sample values, and key relations, and notes that robustness to noisy or outdated metadata is unevaluated.
  • domain assumption Gold database, table, and column annotations in the benchmarks are correct and complete.
    Recall and EM are computed against these gold annotations; any annotation errors would directly affect the reported scores.
  • domain assumption The normalized probability of the 'yes' token is a reliable database relevance score.
    Section 4.2 defines s(D_i|q,h) using next-token logits over yes/no token sets; this is a common confidence heuristic but is not separately validated in the paper.

how reviews work

0 comments
Cite this review

Pith. "Pith review of MDB-Link: Hierarchical Schema Linking for Multi-Database Text-to-SQL." pith.science (2026). https://pith.science/paper/TV3KT2ID

@misc{pith2026260809588,
  author       = {Pith},
  title        = {Pith review of: MDB-Link: Hierarchical Schema Linking for Multi-Database Text-to-SQL},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TV3KT2ID}},
  note         = {Machine review of arXiv:2608.09588}
}
read the original abstract

Traditional Text-to-SQL research and benchmarks assume a known target database, overlooking settings in which a query must be routed within a large, heterogeneous database collection. We therefore study schema linking in a multi-database setting, where the system must first locate the target database and then construct a compact, SQL-relevant schema for generation. We propose MDB-Link, a hierarchical schema-linking framework that retrieves question-relevant columns from a global index, aggregates retrieval evidence to shortlist databases, and uses a budget-aware large language model (LLM) for database reranking, table selection, and column grounding. With Qwen2.5-14B, MDB-Link outperforms LinkAlign on MMQA, Spider2-Snow, and BIRD-dev in database localization and column selection while producing schema subsets close in size to the gold schemas. Exact match improves from 16.88 to 51.41 on MMQA, 2.50 to 9.17 on Spider2-Snow, and 12.52 to 38.01 on BIRD-dev. MDB-Link also runs faster than LinkAlign and AutoLink, demonstrating the effectiveness of hierarchical schema reduction for downstream SQL generation.

Figures

Figures reproduced from arXiv: 2608.09588 by the authors.

Figure 1
Figure 1. The MDB-Link framework comprises offline construction of a global column-wise vector index and [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Indexed point and retrieval document for [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Indexed point and retrieval document for [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

51 extracted references · 22 canonical work pages

  1. [1]

    International Conference on Learning Representations , volume=

    Spider 2.0: Evaluating language models on real-world enterprise text-to-sql workflows , author=. International Conference on Learning Representations , volume=. 2025 , url=

    work page 2025

  2. [2]

    Proceedings of the 2018 conference on empirical methods in natural language processing , pages=

    Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task , author=. Proceedings of the 2018 conference on empirical methods in natural language processing , pages=

    2018

  3. [3]

    2025 , eprint=

    Qwen3 Technical Report , author=. 2025 , eprint=

    2025

  4. [4]

    World Wide Web , volume=

    GenOM: ontology matching with description generation and large language models , author=. World Wide Web , volume=. 2026 , publisher=

    work page 2026

  5. [5]

    arXiv preprint arXiv:2207.05221 , year=

    Language Models (Mostly) Know What They Know , author=. arXiv preprint arXiv:2207.05221 , year=

    arXiv

  6. [6]

    Prompting

    Si, Chenglei and Gan, Zhe and Yang, Zhengyuan and Wang, Shuohang and Wang, Jianfeng and Boyd-Graber, Jordan and Wang, Lijuan , journal=. Prompting. 2022 , url=

    work page 2022

  7. [7]

    arXiv preprint arXiv:1910.14424 , year=

    Multi-stage document ranking with BERT , author=. arXiv preprint arXiv:1910.14424 , year=

    arXiv 1910

  8. [8]

    ACM Transactions on Information Systems (TOIS) , volume=

    Semantic models for the first-stage retrieval: A comprehensive review , author=. ACM Transactions on Information Systems (TOIS) , volume=. 2022 , publisher=. doi:10.1145/3486250 , url=

    doi:10.1145/3486250 2022

Show all 51 references
  1. [9]

    International Conference of the Cross-Language Evaluation Forum for European Languages , pages=

    How relevant is the long tail? a relevance assessment study on million short , author=. International Conference of the Cross-Language Evaluation Forum for European Languages , pages=. 2016 , organization=. doi:10.1007/978-3-319-44564-9_20 , url=

    2016 doi

  2. [10]

    arXiv preprint arXiv:2312.10997 , volume=

    Retrieval-augmented generation for large language models: A survey , author=. arXiv preprint arXiv:2312.10997 , volume=

    arXiv

  3. [11]

    Advances in neural information processing systems , volume=

    Retrieval-augmented generation for knowledge-intensive nlp tasks , author=. Advances in neural information processing systems , volume=. 2020 , url=

    2020

  4. [12]

    Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP) , pages=

    Dense passage retrieval for open-domain question answering , author=. Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP) , pages=. 2020 , url=

    2020

  5. [13]

    The VLDB Journal , volume=

    A survey on deep learning approaches for text-to-SQL , author=. The VLDB Journal , volume=. 2023 , publisher=. doi:10.1007/s00778-022-00776-8 , url=

    2023 doi

  6. [14]

    S ^2 SQL : Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to- SQL Parsers

    Hui, Binyuan and Geng, Ruiying and Wang, Lihan and Qin, Bowen and Li, Yanyang and Li, Bowen and Sun, Jian and Li, Yongbin. S ^2 SQL : Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to- SQL Parsers. Findings of the Association for Computational Linguisti...

    2022 doi

  7. [15]

    LGESQL : Line Graph Enhanced Text-to- SQL Model with Mixed Local and Non-Local Relations

    Cao, Ruisheng and Chen, Lu and Chen, Zhi and Zhao, Yanbin and Zhu, Su and Yu, Kai. LGESQL : Line Graph Enhanced Text-to- SQL Model with Mixed Local and Non-Local Relations. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th Int...

    2021 doi

  8. [16]

    Proceedings of the 58th annual meeting of the association for computational linguistics , pages=

    Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers , author=. Proceedings of the 58th annual meeting of the association for computational linguistics , pages=. 2020 , url=

    2020

  9. [17]

    arXiv preprint arXiv:2308.15363 , year=

    Text-to-sql empowered by large language models: A benchmark evaluation , author=. arXiv preprint arXiv:2308.15363 , year=

    arXiv

  10. [18]

    Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=

    Sqlprompt: In-context text-to-sql with minimal labeled data , author=. Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=. 2023 , url=

    2023

  11. [19]

    arXiv preprint arXiv:2502.00675 , year=

    ReFoRCE: a text-to-SQL agent with self-refinement, consensus enforcement, and column exploration , author=. arXiv preprint arXiv:2502.00675 , year=

    arXiv

  12. [20]

    Advances in neural information processing systems , volume=

    Din-sql: Decomposed in-context learning of text-to-sql with self-correction , author=. Advances in neural information processing systems , volume=. 2023 , url=

    2023

  13. [21]

    Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) , pages=

    Typesql: Knowledge-based type-aware neural text-to-sql generation , author=. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) , pages=

    2018

  14. [22]

    arXiv preprint arXiv:1711.04436 , year=

    Sqlnet: Generating structured queries from natural language without reinforcement learning , author=. arXiv preprint arXiv:1711.04436 , year=

    arXiv

  15. [23]

    arXiv preprint arXiv:1709.00103 , year=

    Seq2sql: Generating structured queries from natural language using reinforcement learning , author=. arXiv preprint arXiv:1709.00103 , year=

    arXiv

  16. [24]

    Proceedings of the 8th international conference on Intelligent user interfaces , pages=

    Towards a theory of natural language interfaces to databases , author=. Proceedings of the 8th international conference on Intelligent user interfaces , pages=

  17. [25]

    Proceedings of the 2014 ACM SIGMOD international conference on Management of data , pages=

    NaLIR: an interactive natural language interface for querying relational databases , author=. Proceedings of the 2014 ACM SIGMOD international conference on Management of data , pages=

    2014

  18. [26]

    Qwen2.5: A Party of Foundation Models , url =

    Qwen Team , month =. Qwen2.5: A Party of Foundation Models , url =

  19. [27]

    2026 , howpublished =

    Qdrant: Vector Search Engine and Vector Database , author =. 2026 , howpublished =

    2026

  20. [28]

    arXiv preprint arXiv:2601.03267 , year=

    Openai gpt-5 system card , author=. arXiv preprint arXiv:2601.03267 , year=

    arXiv

  21. [29]

    arXiv preprint arXiv:2601.08584 , year=

    Ministral 3 , author=. arXiv preprint arXiv:2601.08584 , year=

    arXiv

  22. [30]

    arXiv preprint arXiv:2506.05176 , year=

    Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models , author=. arXiv preprint arXiv:2506.05176 , year=

    arXiv

  23. [31]

    The thirteenth international conference on learning representations , year=

    MMQA: Evaluating LLMs with multi-table multi-hop complex questions , author=. The thirteenth international conference on learning representations , year=

  24. [32]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    AutoLink: Autonomous Schema Exploration and Expansion for Scalable Schema Linking in Text-to-SQL at Scale , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=. 2026 , doi=

    2026

  25. [33]

    arXiv preprint arXiv:2507.23104 , year=

    RASL: Retrieval Augmented Schema Linking for Massive Database Text-to-SQL , author=. arXiv preprint arXiv:2507.23104 , year=

    arXiv

  26. [34]

    Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    Linkalign: Scalable schema linking for real-world large-scale multi-database text-to-sql , author=. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=. 2025 , url=

    2025

  27. [35]

    arXiv preprint arXiv:2405.16755 , year=

    Chess: Contextual harnessing for efficient sql synthesis , author=. arXiv preprint arXiv:2405.16755 , year=

    arXiv

  28. [36]

    S chema G raph SQL : Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to- SQL on Large-Scale Databases

    Safdarian, AmirHossein and Mohammadi, Milad and Bashirloo, Ehsan Jahanbakhsh and Naderi, Mona Shahamat and Faili, Heshaam. S chema G raph SQL : Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to- SQL on Large-Scale Databases. Findings of the A ssociation fo...

    2026 doi

  29. [37]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Resdsql: Decoupling schema linking and skeleton parsing for text-to-sql , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=. 2023 , doi=

    2023

  30. [38]

    A big bench for large-scale database grounded text-to-SQLs , volume=

    Can llm already serve as a database interface , author=. A big bench for large-scale database grounded text-to-SQLs , volume=

  31. [39]

    Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages=

    Re-examining the role of schema linking in text-to-SQL , author=. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages=

    2020

  32. [40]

    Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

    Clancey, William J. Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83)

  33. [41]

    Classification Problem Solving

    Clancey, William J. Classification Problem Solving. Proceedings of the Fourth National Conference on Artificial Intelligence

  34. [42]

    , title =

    Robinson, Arthur L. , title =. 1980 , doi =. https://science.sciencemag.org/content/208/4447/1019.full.pdf , journal =

    1980

  35. [43]

    New Ways to Make Microcircuits Smaller---Duplicate Entry

    Robinson, Arthur L. New Ways to Make Microcircuits Smaller---Duplicate Entry. Science

  36. [44]

    Clancey and Glenn Rennels , abstract =

    Diane Warner Hasling and William J. Clancey and Glenn Rennels , abstract =. Strategic explanations for a diagnostic consultation system , journal =. 1984 , issn =. doi:https://doi.org/10.1016/S0020-7373(84)80003-6 , url =

    1984 doi

  37. [45]

    and Rennels, Glenn R

    Hasling, Diane Warner and Clancey, William J. and Rennels, Glenn R. and Test, Thomas. Strategic Explanations in Consultation---Duplicate. The International Journal of Man-Machine Studies

  38. [46]

    Poligon: A System for Parallel Problem Solving

    Rice, James. Poligon: A System for Parallel Problem Solving

  39. [47]

    Transfer of Rule-Based Expertise through a Tutorial Dialogue

    Clancey, William J. Transfer of Rule-Based Expertise through a Tutorial Dialogue

  40. [48]

    The Engineering of Qualitative Models

    Clancey, William J. The Engineering of Qualitative Models

  41. [49]

    2017 , eprint=

    Attention Is All You Need , author=. 2017 , eprint=

    2017

  42. [50]

    Pluto: The 'Other' Red Planet

    NASA. Pluto: The 'Other' Red Planet

  43. [51]

    Li, Jinyang and Hui, Binyuan and Qu, Ge and Yang, Jiaxi and Li, Binhua and Li, Bowen and Wang, Bailin and Qin, Bowen and Geng, Ruiying and Huo, Nan and Zhou, Xuanhe and Ma, Chenhao and Li, Guoliang and Chang, Kevin and Huang, Fei and Cheng, Reynold and Li, Yongbin , booktitle ...

    2023

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.