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REVIEW 2 major objections 2 minor 24 references

Schema structure changes cause LLMs to generate different SQL answers even when the underlying data and questions stay the same.

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A new evaluation framework generates equivalent schemas from an E/R model and shows that LLMs produce SQL queries with different answers across those schemas for fixed questions and data.

T0 review reviewed 2026-06-29 challenge →

load-bearing objection The paper shows LLMs for text-to-SQL give inconsistent answers across structurally different but supposedly equivalent schemas, using a new E/R-based generation method. the 2 major comments →

arxiv 2605.25838 v1 pith:V2535XHT submitted 2026-05-25 cs.DB

Same Data, Different Schemas: Robustness of LLM-based Text-to-SQL

Nitin Kanchinadam , Aditya Menachery , Amol Deshpande This is my paper
classification cs.DB
keywords text-to-SQLLLM robustnessschema variationsequivalent schemasE/R model shreddingdatabase benchmarks
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

The paper introduces a method to create multiple relational schemas that represent identical information by starting from one entity-relationship diagram and applying different shredding choices. It then runs the same natural-language questions and data against four leading LLMs on these variants and measures how often the produced SQL queries return different results. The experiments show that schema differences alone drive large inconsistencies in model outputs. Supplying the original entity-relationship diagram as extra context reduces but does not eliminate the inconsistencies. The framework also yields a systematic way to produce many synthetic training examples for future models.

Core claim

By generating conceptually equivalent schemas through controlled variation of shredding choices from a shared E/R model, the authors demonstrate that LLM text-to-SQL systems produce SQL queries whose results differ substantially across those schemas even though the natural language questions and stored data remain fixed.

What carries the argument

A framework that translates one E/R model into multiple relational schemas by varying shredding choices, keeping semantics identical while changing structure.

Load-bearing premise

Different shredding choices produce schemas that differ only in structure while preserving exactly the same meaning, so any change in LLM output can be blamed on the schema rather than on hidden semantic differences.

What would settle it

Run the same questions on two schema variants generated from the same E/R model and observe that every LLM returns identical result sets for every question.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Current text-to-SQL benchmarks that use only one schema per domain understate the robustness problem.
  • Providing the original E/R diagram as context improves consistency but leaves residual inconsistencies.
  • Systematic schema variation can be used to generate large numbers of synthetic training examples.
  • One practical mitigation is to produce multiple candidate SQL queries by querying the same question against several schema variants.

Where Pith is reading between the lines

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

  • If the effect holds across more domains, text-to-SQL systems may need explicit mechanisms to normalize or canonicalize schema presentation before inference.
  • The same framework could be applied to test whether other structured-prediction tasks, such as semantic parsing, show comparable sensitivity to input representation.
  • Training on data augmented by schema variants might reduce the observed inconsistencies without requiring changes at inference time.
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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

2 major / 2 minor

Summary. The paper introduces a framework to evaluate LLM robustness in text-to-SQL by generating multiple structurally distinct relational schemas from a shared E/R model via different shredding choices. These variants are claimed to preserve identical semantics while the NL questions and underlying data remain fixed. The authors evaluate four LLMs on two domains, report frequent inconsistencies in generated SQL answers across schemas via pairwise heatmaps, note that E/R context helps but does not eliminate issues, and suggest the approach for synthetic data generation and robustness improvement.

Significance. If the semantic equivalence of the schema variants is established, the work would usefully expose a gap in existing text-to-SQL benchmarks and provide a controlled method for testing structural sensitivity. The heatmap-based consistency analysis and the proposal to use systematic schema variation for training data are constructive contributions.

major comments (2)
  1. [Section 3] Section 3 (Framework): The description of schema generation via shredding does not include an explicit verification procedure (e.g., canonical query generation per NL question, cross-schema result-set comparison, or data-mapping checks) to confirm that all variants preserve identical semantics for the evaluated questions. Without this, differences in LLM outputs cannot be attributed solely to structure.
  2. [Section 4] Section 4 (Experiments): The consistency claims and heatmaps rest on the unverified equivalence assumption; if any shredding choice alters effective semantics (e.g., via nullability or join-table effects), the reported answer divergences may reflect semantic mismatch rather than robustness failure.
minor comments (2)
  1. [Results section] The heatmaps in the results section would benefit from explicit axis labels, a color-scale legend, and a definition of the consistency metric used.
  2. A short table summarizing the number of schema variants, questions, and domains would improve readability of the experimental setup.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the importance of explicit semantic equivalence verification. We address each major comment below and will revise the manuscript accordingly to strengthen the framework description and experimental claims.

read point-by-point responses

  1. Referee: [Section 3] Section 3 (Framework): The description of schema generation via shredding does not include an explicit verification procedure (e.g., canonical query generation per NL question, cross-schema result-set comparison, or data-mapping checks) to confirm that all variants preserve identical semantics for the evaluated questions. Without this, differences in LLM outputs cannot be attributed solely to structure.

    Authors: We agree that an explicit verification procedure would make the equivalence claim more rigorous. The current manuscript relies on equivalence by construction: all schemas are derived from the same E/R model via different shredding choices, which by definition preserve the underlying conceptual semantics and data. However, to directly address the concern, we will add a new subsection in Section 3 describing a verification procedure. This will include (1) generating canonical SQL queries for each NL question against a reference schema, (2) executing them on all variants to confirm identical result sets, and (3) data-mapping checks to ensure no information loss from shredding choices such as nullability or join tables. This addition will allow readers to confirm that observed divergences are due to schema structure rather than semantic mismatch. revision: yes

  2. Referee: [Section 4] Section 4 (Experiments): The consistency claims and heatmaps rest on the unverified equivalence assumption; if any shredding choice alters effective semantics (e.g., via nullability or join-table effects), the reported answer divergences may reflect semantic mismatch rather than robustness failure.

    Authors: We acknowledge that without explicit verification, the consistency analysis could be confounded. As noted in our response to the Section 3 comment, we will incorporate the verification procedure into the revised manuscript. Once added, the heatmaps and consistency metrics in Section 4 will be supported by confirmed semantic equivalence across all schema variants for the evaluated questions and domains. We will also include a brief discussion of potential edge cases (e.g., nullability effects) and how the verification mitigates them. This revision ensures the reported LLM inconsistencies can be confidently attributed to structural sensitivity. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical framework is self-contained.

full rationale

The paper introduces an evaluation framework that generates schema variants from a shared E/R model via shredding choices, then measures LLM consistency on fixed NL questions and data. No equations, fitted parameters, predictions, or derivations are present. The central claim rests on the stated construction that variants preserve semantics (an assumption, not a reduction to prior fitted results or self-citations). No load-bearing self-citation chains or ansatzes appear. The method is presented as an independent benchmarking tool rather than a result derived from its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; the central claim rests on the unelaborated premise that shredding variants preserve semantics exactly.

reviewed 2026-06-29 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Same Data, Different Schemas: Robustness of LLM-based Text-to-SQL." pith.science (2026). https://pith.science/paper/V2535XHT

@misc{pith2026260525838,
  author       = {Pith},
  title        = {Pith review of: Same Data, Different Schemas: Robustness of LLM-based Text-to-SQL},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V2535XHT}},
  note         = {Machine review of arXiv:2605.25838}
}
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read the original abstract

Large language models (LLMs) consistently achieve strong results on text-to-SQL benchmarks, but their robustness to schema variations remains poorly understood. Recent work suggests that the schema structure matters, but does not provide a clear and systematic way to evaluate model behavior when different schemas represent the same underlying data. We address this problem by presenting a framework to evaluate and benchmark text-to-SQL techniques over equivalent relational schemas generated from a common E/R model. By varying the ``shredding'' choices used to translate the conceptual design into relations, we create multiple schema variants that differ structurally while preserving the same underlying semantics. This gives us a controlled setting in which the natural language questions and data remain fixed, and only the schema changes. We use this framework to evaluate four leading LLMs on the same questions across multiple schema variants (for two separate domains), and summarize consistency patterns using pairwise comparison heatmaps. Our results show that schema structure significantly affects LLM behavior: across conceptually equivalent schemas, models often produce SQL queries with very different answers. We also find that providing additional context (specifically, the original E/R specification) improves the performance, but does not fully ameliorate the inconsistencies. In addition to demonstrating that the current text-to-SQL evaluations miss an important notion of robustness, our framework provides a way to generate a large number of synthetic datasets that can be used to train new models, and suggests a mechanism to make text-to-SQL more robust by generating additional candidate plans for a given natural language query through systematic schema variations.

Figures

Figures reproduced from arXiv: 2605.25838 by Aditya Menachery, Amol Deshpande, Nitin Kanchinadam.

Figure 1
Figure 1. Figure 1: Overall benchmark generation and evaluation pipeline. Most of the steps, including initial E/R diagram, can themselves [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Social Media E/R diagram used as a running example. We omit relationship names for brevity. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Pairwise agreement heatmaps for all models for [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Agreement by question across schemas for each [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: Pairwise agreement heatmaps for all models for [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Pairwise agreement heatmaps for all models for the [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: E/R Diagram for the Retail Dataset (11) List the 5 customers with the highest total spend. Define total spend as the sum of the prices of all products that appear in orders the customer has placed: for each order the customer has placed, add the price of every product in that order (a product appearing in two different orders of the same customer is counted twice, once per order). Return the customer’s fir… view at source ↗

discussion (0)

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Reference graph

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This paper was first reviewed by grok-4.3 on June 29, 2026.