arxiv: 2605.10318 · v1 · submitted 2026-05-11 · 💻 cs.CL

Recognition: 2 theorem links

· Lean Theorem

Extending Confidence-Based Text2Cypher with Grammar and Schema Aware Filtering

Makbule Gulcin Ozsoy

Authors on Pith no claims yet

Pith reviewed 2026-05-12 05:02 UTC · model grok-4.3

classification 💻 cs.CL

keywords Text2CypherLarge Language ModelsQuery GenerationGrammar ValidationSchema ConstraintsPost-generation FilteringNatural Language to Graph Query

0 comments

The pith

Post-generation grammar and schema filters raise syntactic validity and execution success for LLM-generated Cypher queries.

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

The paper tests whether adding structured checks after an LLM produces a Cypher query can make the output more reliable for database use. It extends an existing confidence-based inference method by running generated queries through grammar validation followed by schema consistency checks before choosing the final answer. Experiments on two instruction-tuned models show grammar filtering cuts down syntactically broken queries while schema filtering lifts the share of queries that actually run correctly against the database. The same filters also produce more cases with no output at all and lower overall coverage. The results indicate that simple test-time structural constraints can improve Text2Cypher reliability without any model retraining.

Core claim

Extending the confidence-based Text2Cypher framework with a sequential filtering process that applies grammar validation and schema constraints after generation improves syntactic validity and execution quality on the tested models and datasets, while increasing the rate of empty predictions.

What carries the argument

The sequential filtering process that combines confidence scoring, grammar validation, and schema constraints before final aggregation.

If this is right

Grammar-based filtering alone increases the share of syntactically valid Cypher queries.
Schema-aware filtering on top of grammar checks further raises the rate at which queries execute successfully against the target database.
Stronger filtering raises the number of empty predictions and reduces execution coverage.
Syntax and schema constraints contribute differently to overall query correctness and can be measured separately.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same post-generation filter sequence could be applied to Text2SQL or Text2SPARQL pipelines to test whether the validity gains transfer across query languages.
Adjusting filter strictness dynamically according to model confidence might reduce empty predictions while keeping most of the quality benefit.
Running the filters inside the decoding loop rather than only after full generation could change how often the model produces valid candidates in the first place.

Load-bearing premise

The gains from grammar and schema filtering will continue to appear with models and datasets beyond the two tested here, and the accompanying rise in empty predictions will stay tolerable in real applications.

What would settle it

Apply the identical grammar-plus-schema filtering pipeline to a third instruction-tuned model on a fresh graph database and check whether syntactic validity and execution success still increase by comparable amounts.

Figures

Figures reproduced from arXiv: 2605.10318 by Makbule Gulcin Ozsoy.

**Figure 1.** Figure 1: Overview of the filtering pipeline, where confidence-, grammar-, and schema-based steps are applied sequentially to remove invalid or low-quality queries before aggregation. schema information in the input prompt or during decoding, while using it as a post-generation filtering step remains less explored. In this work, we study how structured constraints can be incorporated into test-time inference for Tex… view at source ↗

read the original abstract

Large language models (LLMs) allow users to query databases using natural language by translating questions into executable queries. Despite strong progress on tasks such as Text2SQL, Text2SPARQL, and Text2Cypher, most existing methods focus on better prompting, fine-tuning, or iterative refinement. However, they often do not explicitly enforce structural constraints, such as syntactic validity and schema consistency. This can reduce reliability, since generated queries must satisfy both syntax rules and database schema constraints to be executable. In this work, we study how structured constraints can be used in test-time inference for Text2Cypher. We focus on post-generation validation to improve query correctness. We extend a confidence-based inference framework with a sequential filtering process that combines confidence scoring, grammar validation, and schema constraints before final aggregation. This lets us analyze how different constraint types affect generated queries. Our experiments with two instruction-tuned models show that grammar-based filtering improves syntactic validity. Schema-aware filtering further improves execution quality by enforcing consistency with the database structure. However, stronger filtering also increases the number of empty predictions and reduces execution coverage. Overall, we show that adding simple structural checks at test time improves the reliability of Text2Cypher generation, and we provide a clearer view of how syntax and schema constraints contribute differently.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Grammar and schema filters raise syntactic and execution quality in Text2Cypher but the paper leaves the coverage trade-off only partially addressed.

read the letter

The main thing to know is that this work takes an existing confidence-based generation setup and layers on a simple sequential filter—grammar validation first, then schema constraints—to catch invalid Cypher queries before they reach execution. Experiments on two instruction-tuned models show the grammar step lifts syntactic validity and the schema step improves execution success, which is a clean separation of concerns not emphasized in prior Text2Cypher papers they cite. They also flag that stronger filtering produces more empty outputs and lower coverage, which keeps the discussion grounded rather than oversold. That breakdown of constraint types is the clearest contribution here and should be useful for anyone tuning test-time inference on graph queries. The experiments stay modest in scope, which matches the applied nature of the task. The soft spot is the evaluation of the trade-off. The abstract notes the rise in empties but does not spell out an aggregate metric that treats empty predictions as failures or compares overall task success rates against baselines. If the reported gains are only conditional on non-empty outputs, the reliability claim depends on an assumption that users will accept the coverage loss; that assumption is stated but not stress-tested with utility numbers or user-facing metrics. Generalization beyond the two models and whatever datasets were used is also left open, though that is typical for this style of paper. This is for engineers building natural-language interfaces to Neo4j-style databases who need low-cost reliability tweaks without retraining. Readers already working on Text2SQL or constrained decoding will pick up the practical filtering pipeline quickly. The work is coherent on its own terms and shows honest engagement with the limitations of filtering, so it deserves a serious referee to check the exact numbers and experimental controls. I would send it for review rather than desk reject.

Referee Report

2 major / 1 minor

Summary. The paper extends a confidence-based Text2Cypher inference framework with a sequential post-generation filtering process that applies grammar validation followed by schema constraints. Experiments on two instruction-tuned models indicate that grammar filtering improves syntactic validity and schema filtering further improves execution quality, while noting that stronger filtering increases empty predictions and reduces coverage.

Significance. If substantiated, the work would usefully demonstrate the differential contributions of syntax and schema constraints to reliability in LLM-based graph query generation, offering a practical test-time approach that complements prompting or fine-tuning methods.

major comments (2)

[§4 (Experiments)] §4 (Experiments): The abstract and results report only directional improvements from grammar and schema filters, with no quantitative effect sizes, baseline comparisons to unconstrained generation, statistical tests, or explicit handling of empty predictions in the metrics. This prevents verification of the claimed gains.
[§4 (Experiments)] §4 (Experiments): The reliability improvement claim is load-bearing on showing net benefit, yet the evaluation appears to rely on conditional metrics over non-empty outputs. No aggregate metric (e.g., overall success rate treating empties as failures) is described to demonstrate that per-prediction gains outweigh the documented coverage loss.

minor comments (1)

[Abstract] Abstract: The sequential filtering process (confidence scoring then grammar then schema) would benefit from a short pseudocode or diagram to clarify the exact pipeline and aggregation step.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We agree that the experimental section would benefit from more quantitative detail and aggregate metrics to better substantiate the claims. We will revise accordingly.

read point-by-point responses

Referee: The abstract and results report only directional improvements from grammar and schema filters, with no quantitative effect sizes, baseline comparisons to unconstrained generation, statistical tests, or explicit handling of empty predictions in the metrics. This prevents verification of the claimed gains.

Authors: We acknowledge that the current presentation emphasizes directional trends. In the revised version we will report quantitative effect sizes (absolute and relative percentage changes in syntactic validity and execution success), explicit baseline comparisons against unconstrained generation, and statistical significance tests (e.g., McNemar’s test for paired binary outcomes). Empty predictions will be explicitly defined as failures in all coverage and success calculations, with a dedicated column or footnote clarifying their treatment. revision: yes
Referee: The reliability improvement claim is load-bearing on showing net benefit, yet the evaluation appears to rely on conditional metrics over non-empty outputs. No aggregate metric (e.g., overall success rate treating empties as failures) is described to demonstrate that per-prediction gains outweigh the documented coverage loss.

Authors: We agree that net benefit must be demonstrated. While the manuscript already notes the coverage trade-off, we will add an aggregate success rate metric that treats every empty output as a failure. This overall rate will be reported alongside the conditional (non-empty) metrics, together with a breakdown table showing how validity and execution gains compare against the coverage reduction for each filtering stage. revision: yes

Circularity Check

0 steps flagged

No circularity; results are empirical measurements on held-out data

full rationale

The paper extends a prior confidence-based Text2Cypher framework by adding post-generation grammar validation and schema constraints, then reports experimental outcomes on syntactic validity and execution quality for two instruction-tuned models. All central claims rest on direct performance metrics computed from test-set outputs rather than any derivation, equation, fitted parameter, or self-citation that reduces the result to its own inputs by construction. No uniqueness theorems, ansatzes, or renamings of known results are invoked as load-bearing steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical axioms, free parameters, or invented entities are introduced; the work is an empirical study of post-processing filters on LLM outputs.

pith-pipeline@v0.9.0 · 5524 in / 1045 out tokens · 57186 ms · 2026-05-12T05:02:14.548352+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear
We extend a confidence-based inference framework with a sequential filtering process that combines confidence scoring, grammar validation, and schema constraints before final aggregation.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
grammar-based filtering improves syntactic validity. Schema-aware filtering further improves execution quality

Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages

[1]

G. Y. Zhu, W. Shao, X. Zhu, L. Yu, J. Guo, X. Cheng, Text2sql: Pure fine-tuning and pure knowledge distillation, in: NAACL 2025, 2025

work page 2025
[2]

Sennrich, S

K. Sennrich, S. Ahmadi, Conversational lexicography: Querying lexicographic data on knowledge graphs with sparql through natural language, in: Proceedings of the 5th Conference on Language, Data and Knowledge, 2025, pp. 289–300

work page 2025
[3]

M. G. Ozsoy, L. Messallem, J. Besga, G. Minneci, Text2cypher: Bridging natural language and graph databases, in: COLING 2025, 2025

work page 2025
[4]

Bunkova, L

O. Bunkova, L. Di Fruscia, S. Rupprecht, A. M. Schweidtmann, M. J. Reinders, J. M. Weber, Ground- ing large language models in reaction knowledge graphs for synthesis retrieval, arXiv preprint arXiv:2601.16038 (2026)

work page arXiv 2026
[5]

Mandilara, C

I. Mandilara, C. M. Androna, E. Fotopoulou, A. Zafeiropoulos, S. Papavassiliou, Decoding the mystery: How can llms turn text into cypher in complex knowledge graphs?, IEEE Access (2025)

work page 2025
[6]

C. Yang, C. Li, X. Hu, H. Yu, J. Lu, Enhancing knowledge graph interactions: A comprehensive text-to-cypher pipeline with large language models, Inf. Process. Manag. 63 (2026) 104280

work page 2026
[7]

X. Wang, J. Wei, D. Schuurmans, Q. V. Le, E. H. Chi, S. Narang, A. Chowdhery, D. Zhou, Self- consistency improves chain of thought reasoning in language models, in: The Eleventh Inter- national Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, OpenReview.net, 2023. URL: https://openreview.net/forum?id=1PL1NIMMrw

work page 2023
[8]

Y. Fu, X. Wang, Y. Tian, J. Zhao, Deep think with confidence, arXiv preprint arXiv:2508.15260 (2025)

work page arXiv 2025
[9]

Dessi, M

R. Dessi, M. G. Ozsoy, Improving text2cypher with confidence-based test-time strategies, in: Proceedings of the KG–LLM Workshop at LREC-COLING 2026, 2026. URL: https://kg-llm.github. io/program/pdf/2026.kgllmlrec26-1.5.pdf, to appear

work page 2026
[10]

Tuccio, L

G. Tuccio, L. Bulla, M. Madonia, A. Gangemi, et al., Grammar-llm: Grammar-constrained natural language generation, in: Findings of the Association for Computational Linguistics: ACL 2025, 2025, pp. 3412–3422

work page 2025
[11]

S. Geng, M. Josifoski, M. Peyrard, R. West, Grammar-constrained decoding for structured nlp tasks without finetuning, in: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023, pp. 10932–10952

work page 2023
[12]

K. Park, J. Wang, T. Berg-Kirkpatrick, N. Polikarpova, L. D’Antoni, Grammar-aligned decoding, Advances in Neural Information Processing Systems 37 (2024) 24547–24568

work page 2024
[13]

Raspanti, T

F. Raspanti, T. Ozcelebi, M. Holenderski, Grammar-constrained decoding makes large language models better logical parsers, in: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), 2025, pp. 485–499

work page 2025
[14]

H. A. Caferoğlu, Ö. Ulusoy, E-sql: Direct schema linking via question enrichment in text-to-sql, arXiv preprint arXiv:2409.16751 (2024)

work page arXiv 2024
[15]

Chung, G

Y. Chung, G. T. Kakkar, Y. Gan, B. Milne, F. Ozcan, Is long context all you need? leveraging llm’s extended context for nl2sql, arXiv preprint arXiv:2501.12372 (2025)

work page arXiv 2025
[16]

B. Qin, B. Hui, L. Wang, M. Yang, J. Li, B. Li, R. Geng, R. Cao, J. Sun, L. Si, et al., A survey on text-to-sql parsing: Concepts, methods, and future directions, arXiv preprint arXiv:2208.13629 (2022)

work page arXiv 2022
[17]

Katsogiannis-Meimarakis, G

G. Katsogiannis-Meimarakis, G. Koutrika, A survey on deep learning approaches for text-to-sql, VLDB J. 32 (2023) 905–936

work page 2023
[18]

Y. Lu, M. Bartolo, A. Moore, S. Riedel, P. Stenetorp, Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, pp. 8086–8098

work page 2022
[19]

Z. Kang, X. Zhao, D. Song, Scalable best-of-n selection for large language models via self-certainty, in: 2nd AI for Math Workshop@ ICML 2025, 2025

work page 2025
[20]

Korikov, P

A. Korikov, P. Du, S. Sanner, N. Rekabsaz, Batched self-consistency improves llm relevance assessment and ranking, in: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025, pp. 32675–32691

work page 2025
[21]

H. Xu, S. Chen, R. Qiu, Y. Yan, C. Luo, M. Cheng, J. He, H. Tong, Prune as you generate: Online rollout pruning for faster and better rlvr, arXiv preprint arXiv:2603.24840 (2026)

work page arXiv 2026
[22]

Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation,

L. Beurer-Kellner, M. Fischer, M. Vechev, Guiding llms the right way: Fast, non-invasive constrained generation, arXiv preprint arXiv:2403.06988 (2024)

work page arXiv 2024
[23]

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

work page Pith review arXiv 2018
[24]

B. Wang, R. Shin, X. Liu, O. Polozov, M. Richardson, Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers, arXiv preprint arXiv:1911.04942 (2019)

work page arXiv 1911
[25]

B. Hui, X. Shi, R. Geng, B. Li, Y. Li, J. Sun, X. Zhu, Improving text-to-sql with schema dependency learning, arXiv preprint arXiv:2103.04399 (2021)

work page arXiv 2021
[26]

K. Xu, Y. Wang, Y. Wang, Z. Wang, Z. Wen, Y. Dong, Sead: End-to-end text-to-sql generation with schema-aware denoising, in: Findings of the Association for Computational Linguistics: NAACL 2022, 2022, pp. 1845–1853

work page 2022
[27]

Pourreza, D

M. Pourreza, D. Rafiei, Din-sql: Decomposed in-context learning of text-to-sql with self-correction, Advances in Neural Information Processing Systems 36 (2023) 36339–36348

work page 2023
[28]

Z. Cao, Y. Zheng, Z. Fan, X. Zhang, W. Chen, X. Bai, Rsl-sql: Robust schema linking in text-to-sql generation, arXiv preprint arXiv:2411.00073 (2024)

work page arXiv 2024
[29]

M. G. Ozsoy, Enhancing text2cypher with schema filtering, arXiv preprint arXiv:2505.05118 (2025)

work page arXiv 2025
[30]

D. Wu, Z. Tang, Y. He, X. Luo, Schemarag: A schema-aware retrieval-augmented generation framework for text-to-sql, Proceedings of the ACM on Management of Data 4 (2026)

work page 2026
[31]

Parr, The Definitive ANTLR 4 Reference, Oreilly and Associate Series, Pragmatic Bookshelf,

T. Parr, The Definitive ANTLR 4 Reference, Oreilly and Associate Series, Pragmatic Bookshelf,

work page
[32]

URL: https://books.google.co.uk/books?id=SBXuLwEACAAJ

work page
[33]

W. Kwon, Z. Li, S. Zhuang, Y. Sheng, L. Zheng, C. H. Yu, J. Gonzalez, H. Zhang, I. Stoica, Efficient memory management for large language model serving with pagedattention, in: J. Flinn, M. I. Seltzer, P. Druschel, A. Kaufmann, J. Mace (Eds.), Proceedings of the 29th Symposium on Operating Systems Principles, SOSP 2023, Koblenz, Germany, October 23-26, 20...

work page 2023