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arxiv: 2606.18108 · v1 · pith:IZQ3WYPRnew · submitted 2026-06-16 · 🌌 astro-ph.IM · cs.AI

Querying an astronomical database using large language models: the ALeRCE text-to-SQL system

P.A. Estevez , J.Espejo-Moreira , S. Sanfeliu-Alvarez , F. Forster , A. M. Munoz Arancibia , G. Cabrera-Vives , F. E. Bauer , A. Bayo
show 5 more authors
M. Catelan R. Dastidar L. Hernandez-Garcia J.A. Intriago G. Pignata
This is my paper

Pith reviewed 2026-06-26 22:33 UTC · model grok-4.3

classification 🌌 astro-ph.IM cs.AI
keywords text-to-SQLlarge language modelsALeRCEastronomical databasenatural language queryingSQL generationin-context learningquery systems
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The pith

Large language models generate executable SQL from natural language for the ALeRCE astronomical database when guided by a four-module step-by-step framework.

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

The paper constructs a dataset of 110 natural language to SQL pairs for the ALeRCE database and develops a text-to-SQL system based on large language models with in-context learning. It introduces a step-by-step generation framework that includes schema linking, query classification, prompt decomposition, and self-correction modules. Evaluation across thirteen LLMs shows the framework outperforms direct inference and that self-correction reduces execution errors, with perfect-match rates highest for simple queries and lower for medium and hard ones.

Core claim

The step-by-step generation framework comprising schema linking, query classification, prompt decomposition, and self-correction modules enables LLMs to produce correct SQL queries from natural language inputs for the ALeRCE schema, consistently outperforming direct-inference baselines while the self-correction module reduces execution errors.

What carries the argument

The four-module step-by-step generation framework that breaks the text-to-SQL task into schema linking, query classification, prompt decomposition, and self-correction.

Load-bearing premise

The hand-constructed set of 110 NL/SQL pairs is representative enough of real user queries to support claims about performance across query complexity levels.

What would settle it

Performance measurements on a new collection of queries submitted by actual ALeRCE users, rather than the hand-crafted test set.

Figures

Figures reproduced from arXiv: 2606.18108 by A. Bayo, A. M. Munoz Arancibia, F. E. Bauer, F. Forster, G. Cabrera-Vives, G. Pignata, J.A. Intriago, J.Espejo-Moreira, L. Hernandez-Garcia, M. Catelan, P.A. Estevez, R. Dastidar, S. Sanfeliu-Alvarez.

Figure 1
Figure 1. Figure 1: Four components of a basic prompt for the T2S task. This [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Diagram of the schema linking, the first step of the pro [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Self-correction step generates a prompt specific to each [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Claude Opus 4.6 perfect matching performance for rows [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: PM performance comparison of direct vs step-by-step [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Performance comparison of the top six LLMs when using [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Number and type of execution errors using Claude Opus [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Percentage of perfect queries, semantic errors, and execution errors for the direct and step-by-step query generation methods [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
read the original abstract

We develop a text-to-SQL (structured query language) system based on large language models (LLMs) using in-context learning and apply it to the Automatic Learning for the Rapid Classification of Events (ALeRCE) astronomical database. ALeRCE is a community broker for the Zwicky Transient Facility and the Vera C. Rubin Observatory. The system enables users to query the database in natural language (NL) and generates executable SQL queries. To develop and evaluate the system, we constructed a dataset of 110 NL/SQL pairs. We propose a step-by-step generation framework comprising four modules: schema linking, query classification, prompt decomposition, and self-correction. The performance of thirteen LLMs is evaluated using in-context learning and prompt engineering techniques. Text-to-SQL performance is assessed using the perfect-match (PM) rate for row identifiers (e.g., object identifiers) and column identifiers (i.e., column names). The proposed step-by-step framework consistently outperforms a direct-inference baseline, while the self-correction module consistently reduces execution errors. For Claude Opus 4.6, PM performance on row (column) identifiers is high for simple queries, reaching 0.97 (0.94), and decreases with query complexity to 0.44 (0.72) for medium queries and 0.59 (0.49) for hard queries. Among the thirteen evaluated models, the best-performing LLMs for the text-to-SQL task are Claude Opus 4.6, Gemini 2.5 Pro, Gemini 3 Flash, and GPT-5.2-Codex.

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.

Referee Report

1 major / 2 minor

Summary. The paper develops a text-to-SQL system for the ALeRCE astronomical database using LLMs and in-context learning. It introduces a step-by-step framework with four modules (schema linking, query classification, prompt decomposition, and self-correction) and evaluates thirteen LLMs on a hand-constructed set of 110 NL/SQL pairs. The framework is reported to outperform a direct-inference baseline, with self-correction reducing execution errors; perfect-match rates on row and column identifiers are given for simple/medium/hard queries, with Claude Opus 4.6, Gemini 2.5 Pro, Gemini 3 Flash, and GPT-5.2-Codex identified as the strongest models.

Significance. If the 110-pair evaluation set is shown to be representative of real ALeRCE user queries, the work would provide a practical demonstration of LLM-based natural-language access to an astronomical broker database, together with an empirical comparison of prompting strategies and the utility of modular decomposition plus self-correction. The multi-model evaluation and concrete PM metrics on row/column identifiers constitute a useful domain-specific benchmark.

major comments (1)
  1. [Dataset construction and evaluation (abstract and methods)] The central performance claims (step-by-step framework outperforming direct inference; self-correction reducing errors; PM rates of 0.97/0.94 on simple queries falling to 0.44/0.72 on medium queries) rest entirely on the 110 hand-constructed NL/SQL pairs stratified by complexity. The manuscript describes the set only as “hand-constructed” and supplies no construction protocol, expert-validation procedure, comparison to actual ALeRCE query logs, or diversity statistics. Because the generalization statements across complexity levels depend on this set being representative, the missing details are load-bearing.
minor comments (2)
  1. [Evaluation metrics] The perfect-match metric is defined on row and column identifiers; a brief explicit statement of how ties or partial matches are scored would improve clarity.
  2. Consider releasing the 110 NL/SQL pairs (with their complexity labels) as supplementary material to support reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The single major comment concerns the description of the evaluation dataset; we address it directly below and will revise the manuscript to incorporate additional information.

read point-by-point responses

  1. Referee: [Dataset construction and evaluation (abstract and methods)] The central performance claims (step-by-step framework outperforming direct inference; self-correction reducing errors; PM rates of 0.97/0.94 on simple queries falling to 0.44/0.72 on medium queries) rest entirely on the 110 hand-constructed NL/SQL pairs stratified by complexity. The manuscript describes the set only as “hand-constructed” and supplies no construction protocol, expert-validation procedure, comparison to actual ALeRCE query logs, or diversity statistics. Because the generalization statements across complexity levels depend on this set being representative, the missing details are load-bearing.

    Authors: We agree that the current manuscript provides insufficient detail on how the 110 NL/SQL pairs were assembled. The pairs were produced by the authors (who have direct experience with the ALeRCE schema and typical broker queries) and were stratified a priori into simple/medium/hard categories according to the number of tables joined, the presence of aggregations or sub-queries, and the number of filtering conditions. Each pair was manually verified for syntactic correctness and semantic alignment with the natural-language intent. To make these steps explicit and allow readers to judge representativeness, we will add a dedicated subsection (approximately one page) to the Methods section that (i) states the stratification criteria with examples, (ii) describes the validation procedure, and (iii) reports basic diversity statistics (distribution of tables used, average number of columns selected, fraction of queries involving joins or aggregations). A direct comparison against live ALeRCE query logs is not feasible for privacy reasons, but the added description will clarify the design choices that were intended to reflect realistic astronomical use cases. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical evaluation on fixed test set

full rationale

The paper reports an empirical comparison of LLM prompting strategies (step-by-step framework vs. direct inference, with/without self-correction) on a fixed collection of 110 author-constructed NL/SQL pairs. No equations, fitted parameters, uniqueness theorems, or ansatzes appear; performance numbers (PM rates stratified by simple/medium/hard) are direct measurements on the supplied test set rather than outputs derived from the same inputs. Self-citations are absent from the load-bearing claims. The work is therefore self-contained against external benchmarks and receives the default non-finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are introduced; the work rests on standard assumptions that LLMs can follow in-context examples and that the 110-pair dataset reflects realistic usage.

pith-pipeline@v0.9.1-grok · 5900 in / 1161 out tokens · 33107 ms · 2026-06-26T22:33:21.944488+00:00 · methodology

discussion (0)

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