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REVIEW 3 major objections 6 minor 1 cited by

CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A corrective second vote on the top two SQL candidates reliably beats plain self-consistency.

desk verdict Practical, incremental text-to-SQL recipe with broad evaluation, but the main comparison is muddied by an unclear revisor setting and mismatched baselines between tables. read the letter →

arxiv 2505.13271 v2 pith:6M5LXFAZ submitted 2025-05-19 cs.CL

Lei Sheng , Shuai-Shuai Xu This is my paper · ORCID
classification cs.CL
keywords text-to-SQLself-consistencyself-correctionmergerevisionGRPOreinforcementlearningexecutionaccuracytest-timescaling
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

The paper proposes a test-time scaling method for text-to-SQL: it says that the usual self-consistency vote—picking the most frequent SQL among parallel samples—often selects a result that is not the one that executes correctly. Instead, CSC-SQL groups the sampled SQL queries by what they return, takes the two largest groups, and asks a merge-revision model to produce a corrected SQL from those two candidates and their execution results; a second self-consistency vote then picks the final answer. The paper further post-trains both the SQL generator and the reviser with GRPO reinforcement learning, using execution accuracy and output format as rewards. Across 11 open models on the BIRD development set, the method reports execution-accuracy gains of 0.72–5.54 percentage points over plain self-consistency, and 71.72% (7B) and 73.67% (32B) on the BIRD private test set. If the paper is right, accuracy at a given model size can be bought with extra sampling plus a small revision step instead of a larger model.

What carries the argument

The load-bearing object is the top-two execution-result group, together with the merge-revision template that is built from those two groups. Concretely: the generator samples N candidate SQLs; each runs against the database; the resulting query outputs are grouped by equality of execution result; the two largest groups contribute one representative SQL each. If the two representatives are identical the system stops, otherwise the schema, question, evidence, both SQLs, and both execution results are placed in a revision prompt that asks the model to produce a corrected answer. A final self-consistency vote over M revised candidates selects the output. The second component is GRPO training, whose reward combines a binary execution-accuracy term (1 if the query matches the gold execution result) with a format term (0.1 for correct answer tags), which the paper says is what turns the revision model from a degrading editor into a genuine corrector.

What would settle it

On the BIRD development set, instrument the pipeline so that for every question you record whether at least one of the top-two execution-result groups contains the gold execution result and whether the final CSC-SQL output is correct when neither does. If the number of questions recovered from two wrong groups is close to zero, or if the reported accuracy gain disappears when the analysis is restricted to questions where the top-two groups exclude the gold result, the correction stage is not the source of the improvement.

Watch

Extended reading notes

Core claim

The central claim is that the failure mode of self-consistency in Text-to-SQL—the most-voted candidate is not always the executable answer—can be repaired by a correction loop that uses execution results as the voting signal. Sampling N SQL queries, executing all of them, grouping by identical execution result, and keeping the top two groups gives the reviser a genuinely alternative candidate, not just a paraphrase of the most-voted query. Feeding those two candidates, their execution outputs, and the schema to a merge-revision model produces M revised queries, and a second execution-based vote selects the final SQL. The paper claims this two-stage scheme outperforms plain self-consistency for every model and sample-size combination tested, that GRPO post-training strengthens both halves of the pipeline, and that the resulting models match or beat larger open models while transferring from BIRD to Spider without retraining.

Load-bearing premise

The premise the framework leans on is that the top two execution-result groups will contain at least one SQL that can answer the question often enough for the reviser to improve on the majority vote; if those groups are both wrong, or nearly identical, the correction step has nothing to recover.

Editorial extensions

If this is right

  • Replacing the plain most-frequent vote with top-two grouping plus merge revision raises execution accuracy on BIRD development by 0.72–5.54 percentage points across 11 open models and all sample sizes tested.
  • Because the gains grow with the number of sampled SQL candidates, users can trade more sampling for more accuracy, and the extra revision cost becomes a smaller share of total inference time.
  • GRPO-trained small models using CSC can beat larger same-family models using plain self-consistency, so the method offers a compute-shifted path to accuracy.
  • The merge-revision model trained only on BIRD transfers to Spider without retraining, showing that the correction behavior generalizes beyond the training benchmark.
  • The 32B configuration reaches 73.67% execution accuracy on the BIRD private test set, which the paper reports as surpassing the base model by 4.64 points.
  • If valid, the scheme can be dropped into other pipelines as a replacement for the self-consistency component.

Reading between the lines

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

  • Editorial inference: the major_top2_pass@k diagnostic introduced for BIRD could be used at inference time as a stopping rule—keep sampling only until the top-two execution groups stop changing, saving compute without giving up the correction step.
  • Editorial inference: the same two-stage revision vote should transfer to other code-generation tasks with executable oracles, such as unit tests or compilers, where grouping by test outcome would supply the same corrective signal; the paper only demonstrates SQL.
  • Editorial inference: a direct test of the GRPO contribution would be to ablate the revision model's reward by using execution accuracy alone, isolating how much of the reported gain comes from the format component.
  • Editorial inference: reported gains are averaged over three runs; a run-by-run variance analysis on the BIRD development set would tell whether the 0.72% lower-end improvements are stable across models.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper proposes CSC-SQL, a test-time scaling method for text-to-SQL generation. Given N candidate SQL queries sampled from a generation model, the method groups them by their execution results, selects the two largest groups, and feeds the representative queries together with the schema and execution outcomes into a merge-revision model. The revision model produces M new candidates, which are then aggregated by self-consistency voting. The authors also apply GRPO reinforcement learning to fine-tune both the SQL generation and revision models, using execution accuracy and a format reward. On the BIRD development set, the method is reported to outperform plain self-consistency by 0.72%-5.54% execution accuracy across 11 open LLMs, and on the BIRD private test set it achieves 71.72% (7B) and 73.67% (32B). The paper also reports cross-dataset generalization on Spider and includes ablations on revision sample count, top-k group size, temperature, and inference cost.

Significance. If the reported results hold, CSC-SQL is a practically useful and inexpensive enhancement to self-consistency for text-to-SQL: it is model-agnostic, built on open models, and the code is publicly released, which is a clear strength. The breadth of the evaluation (11 models, two benchmarks, multiple ablations) and the strong private-test numbers make the contribution potentially valuable to the community. However, the current reporting leaves the central comparison ambiguous: the paper does not specify which revision checkpoint is used in each row of the main table, and several headline numbers are inconsistent with the ablation tables. Until the comparison is made clean and reproducible, the significance of the claimed consistent improvement cannot be fully assessed.

major comments (3)
  1. [§3.2, Table 1 vs. §3.4, Table 2 and Appendix D.2, Table 5] The paper does not state whether the 'CSC' rows in Table 1 are produced with the base merge-revision model or the GRPO-trained revision model, and the reported numbers do not match either column in the ablation tables. For Qwen2.5-Coder-7B-Instruct (no GRPO), Table 1 reports SC=62.18 and CSC=64.28 at n=8, while Table 2 reports SC=61.86 and, at m=8, CSC=61.73 with the base 7B revisor and CSC=63.93 with the GRPO 7B revisor; Table 5 repeats 63.93 for the top-2 configuration. No m value in Table 2 reaches 64.28 (the closest is 64.04 at m=4 or m=16). At n=16, Table 1 reports SC=63.35 and CSC=66.41, while Table 2 and Table 5 report SC=63.65 and CSC=66.78. The SC baseline itself therefore differs between Table 1 and Tables 2/5, and the CSC values in Table 1 are inconsistent with the ablation results. This means the exact numbers supporting the headline claim cannot be reproduced from the ablation tables, and the comparison conflates the corrective-selection mechanism with the effect of the GRPO-trained revisor. Please specify the revision checkpoint used for every row of Table 1 and reconcile the baselines across all tables.
  2. [§3.2 and Table 4] The claim that 'the CSC method consistently outperforms the SC method' is too broad. On the Spider development set, Table 4 shows XiYanSQL-QwenCoder-3B-2502 under GRPO at n=64 with CSC=76.11 versus SC=76.21, i.e., a negative delta of -0.1. More importantly, Table 2 shows that the base (non-GRPO) revisor degrades performance relative to SC (61.73 versus 61.86 at n=8), so the gains in Table 1 for non-GRPO generation models cannot be attributed to the top-two corrective mechanism alone. The paper should either scope the consistency claim to the BIRD development set with the exact configuration used, or provide a controlled decomposition showing the contribution of the top-two selection/revision mechanism over a single-revision baseline using the same revisor.
  3. [§3.1] The paper reports results as 'the average performance over three runs' but does not report variance, error bars, or significance tests. The smallest claimed improvement on BIRD dev is 0.72% (Table 1, n=4 for Meta-Llama-3.1-70B), which corresponds to roughly 11 questions on the 1,534-question development set. With only three runs, a difference of this magnitude can easily fall within run-to-run noise, especially given the internal inconsistency of the baselines across tables. Please report per-cell standard deviations or paired significance tests for the main comparisons in Table 1.
minor comments (6)
  1. [§5 and Figure 1] The paper explicitly acknowledges that CSC relies on sufficient diversity among sampled results, but it does not quantify how often the top two groups both fail. Adding a metric such as 'top2_both_fail@k' alongside major_top2_pass@k would directly measure the premise of the method.
  2. [Figure 3] The four panels of Figure 3 use different y-axis ranges (e.g., 0.575-0.75 for temperature 0.2 versus 0.60-0.85 for temperature 0.8), which makes visual comparison of the temperature effect misleading. A common y-axis would be clearer.
  3. [§3.2] The sentence 'the EX of Qwen2.5-Coder-7B-Instruct without GRPO training improves from 1.28% to 4.25%' is misleading; 1.28% and 4.25% are the gains at n=4 and n=64, not the starting and ending EX values. Please reword.
  4. [Appendix F and Appendix E] The prompt templates and output examples contain a typo 'A VG' instead of 'AVG' (e.g., the <answer> example in Appendix F.1 and the failed case in Section E.2.1).
  5. [References] Several reference names contain spurious spaces, such as 'V olvovsky' and 'V ouitsis'; also 'chose' in Section 2 should be 'choose'.
  6. [Figure 1 caption / §2] The metric major_top2_pass@k is only defined informally in the Figure 1 caption. Please provide a formal definition in the main text or appendix, including whether it is computed over the union of the top two execution-result groups.

Circularity Check

1 steps flagged · score 2.0 of 10

Definitional gap-mitigation metric used as motivation; core EX claims remain external and not circular.

  1. self definitional [Section 1 (Introduction), Figure 1 and caption]
    "To further investigate the behavior of SC, we propose a new metric, major_top2_pass@k, which calculates pass@k based only on the top two voting groups, determined by SQL execution results. As shown in Figure 1, major_top2_pass@k mitigates the gap between self_consistency@k and pass@k by leveraging only the top two candidate groups."

    major_top2_pass@k is defined as pass@k computed over the union of the top two execution-result voting groups. Because this allowed set contains the single top-1 group used by self_consistency@k, the inequality major_top2_pass@k >= self_consistency@k follows by set inclusion, not by empirical measurement; similarly major_top2_pass@k <= pass@k follows because top-2 is a subset of all candidates. Thus the 'gap mitigation' shown in Figure 1 is built into the metric's definition rather than being an independent discovery. This definitional observation motivates the top-2 selection step, but the paper's headline EX improvements are not derived from it: they are measured against gold execution labels on BIRD/Spider, so the central empirical claim remains independent.

full rationale

The derivation chain is: sample N SQLs, group by execution results, take the top two groups, merge-revise with the BASE-SQL template, apply a second Self-Consistency vote, and optionally GRPO-tune generators and revisors with reward R = R_EX + 0.1 * R_format. None of the headline steps reduces to its inputs by construction. R_EX is computed against gold SQL execution results, an external supervised signal, and the reported EX numbers compare against held-out BIRD/Spider labels. The revision model can fail and, as Table 2 shows, a non-GRPO revisor can even degrade performance relative to SC, so the claimed gains are not logically forced. The only self-definitional element is the proposed major_top2_pass@k metric, whose superiority over self_consistency@k is guaranteed by set inclusion; this is used as motivation but is not load-bearing for the main results. The self-citation to Sheng et al. 2025 (BASE-SQL) supplies the merge-revision template as a prompting component and does not serve as evidence for the improvement, so it is not load-bearing. The discrepancy between Table 1 and Tables 2/5 regarding which revisor variant was used is an experimental-reporting and confound concern, not a circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

All central numbers come from benchmark experiments, not from a derivation. The method's effectiveness rests on modeling choices (top-2 grouping, temperature 0.8, revision count 8, format reward weight 0.1) that were selected using the BIRD dev set, and on the standard RL assumption that gold execution results are a reliable reward signal. No new theoretical entities are introduced.

free parameters (4)
  • top_k_group_size = 2
    Selected because Table 5 shows group size 2 gives the best BIRD dev EX among 1, 2, 3; this is a dev-set tuning choice, not derived from theory.
  • sampling_temperature = 0.8
    Chosen from Figure 3 sweep over 0.2, 0.5, 0.8, 1.0; 0.8 and 1.0 perform similarly and 0.8 is used throughout.
  • merge_revision_sample_count = 8
    Default revision sample count used in all main tables; Table 2 varies m but no single optimum is established.
  • format_reward_weight = 0.1
    Equation (3) sets R = R_EX + 0.1 * R_format; no ablation is reported for this weight.
assumptions (5)
  • domain assumption Execution accuracy (EX) on BIRD and Spider is a valid measure of text-to-SQL correctness.
    All headline claims are measured in EX; no separate semantic or human evaluation of SQL quality is reported.
  • domain assumption Grouping candidate SQLs by identical execution results is a sound way to identify semantically equivalent answer groups.
    Section 2 groups candidates by SQL execution results; this assumes identical results imply equivalent answers for voting and revision.
  • domain assumption Gold SQL execution results are available and reliable for computing the RL reward.
    Equation (1) computes the RL reward by comparing generated SQL execution with gold SQL execution on BIRD training data.
  • domain assumption GRPO post-training as implemented in the TRL library improves generation ability as claimed in cited work.
    Appendix B uses the TRL implementation of GRPO with stated hyperparameters; the paper does not re-derive the algorithm.
  • domain assumption The BIRD private test set was not used to select the method or its hyperparameters.
    Test-set numbers are presented as unbiased, but the paper does not state whether test-set feedback influenced any design choice.

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Cite this review

Pith. "Pith review of CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning." pith.science (2026). https://pith.science/paper/6M5LXFAZ

@misc{pith2026250513271,
  author       = {Pith},
  title        = {Pith review of: CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6M5LXFAZ}},
  note         = {Machine review of arXiv:2505.13271}
}
read the original abstract

Large language models (LLMs) have demonstrated strong capabilities in translating natural language questions about relational databases into SQL queries. In particular, test-time scaling techniques such as Self-Consistency and Self-Correction can enhance SQL generation accuracy by increasing computational effort during inference. However, these methods have notable limitations: Self-Consistency may select suboptimal outputs despite majority votes, while Self-Correction typically addresses only syntactic errors. To leverage the strengths of both approaches, we propose CSC-SQL, a novel method that integrates Self-Consistency and Self-Correction. CSC-SQL selects the two most frequently occurring outputs from parallel sampling and feeds them into a merge revision model for correction. Additionally, we employ the Group Relative Policy Optimization (GRPO) algorithm to fine-tune both the SQL generation and revision models via reinforcement learning, significantly enhancing output quality. Experimental results confirm the effectiveness and generalizability of CSC-SQL. On the BIRD private test set, our 7B model achieves 71.72\% execution accuracy, while the 32B model achieves 73.67\%. The code has been open sourced at https://github.com/CycloneBoy/csc_sql.

Figures

Figures reproduced from arXiv: 2505.13271 by the authors.

Figure 1
Figure 1. On the BIRD development set, the trend chart [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the proposed CSC-SQL framework [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. On the BIRD development set, using the Qwen2.5-Coder-7B-Instruct model, when the sampling tempera [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

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Reference graph

Works this paper leans on

65 extracted references · 61 canonical work pages · cited by 1 Pith paper

  1. [1]

    The ‘Longitude‘ column is in the ‘schools‘ table, and the ‘GSoffered‘ column is also in the same table

    **Identify the relevant tables**: We need to access the ‘schools‘ table to get the longitude values and the grade span offered. The ‘Longitude‘ column is in the ‘schools‘ table, and the ‘GSoffered‘ column is also in the same table

    work page

  2. [2]

    This will put the school with the highest longitude at the top

    **Order the schools by longitude in descending order**: To find the school with the highest longitude, we need to order the schools by the ‘Longitude‘ column in descending order. This will put the school with the highest longitude at the top

    work page

  3. [3]

    The VLDB Journal, 32(4):905–936

    A survey on deep learning approaches for text- to-SQL. The VLDB Journal, 32(4):905–936. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023. Ef- ficient memory management for large language model serving with pagedattention. Preprint, arXiv:2309.06180. Dongjun Lee, Choongwon P...

    arXiv 2023

  4. [4]

    **Select the grade span offered**: Finally, we only need to select the ‘GSoffered‘ column from the ‘schools‘ table. Putting it all together, the SQL query will select the ‘GSoffered‘ column from the ‘schools‘ table, order the results by ‘Longitude‘ in descending order, and limit the result to 1 row. </think> <answer> SELECT GSoffered FROM schools ORDER BY...

    work page

  5. [5]

    highest longitude

    **Construct the SQL Query**: - **SELECT**: We need to select the ‘GSoffered‘ column. - **FROM**: We need to specify the ‘schools‘ table. - **ORDER BY**: We sort by ‘Longitude DESC‘ to get the school with the highest longitude at the top. - **LIMIT**: We limit the results to 1 to get only the top result. The draft SQL query provided in the instruction alre...

    work page

  6. [6]

    **Limit the result to the school with the highest longitude**: Since we are only interested in the school with the highest longitude, we can limit the result to just 1 row

    work page

  7. [7]

    **Select the district name**: Finally, we only need to select the district name, which is the ‘District‘ column in the ‘schools‘ table. Given these steps, the SQL query can be constructed as follows: </think> <answer> SELECT T2.District FROM satscores AS T1 INNER JOIN schools AS T2 ON T1.cds = T2.CDSCode WHERE T2.StatusType = ’Active’ GROUP BY T2.District...

    work page

  8. [8]

    The school information, including its longitude, is stored in the ‘schools‘ table

    **Identify the relevant tables and columns**: The question asks for the grade span offered in the school with the highest longitude. The school information, including its longitude, is stored in the ‘schools‘ table. The grade span offered is stored in the ‘GSoffered‘ column of the same table

    work page

Show all 65 references
  1. [9]

    **Determine the order and limit**: To find the school with the highest longitude, we need to order the ‘schools‘ table by the ‘Longitude‘ column in descending order and limit the result to the first row using the ‘LIMIT 1‘ clause

  2. [10]

    What is the grade span offered in the school with the highest longitude?

    **Construct the SQL query**: The SQL query will select the ‘GSoffered‘ column from the ‘schools‘ table, order the results by the ‘Longitude‘ column in descending order, and limit the output to the first row. </think> <answer> SELECT GSoffered FROM schools ORDER BY ABS(Longitud...

  3. [11]

    **Understand the Data**: The table ‘schools‘ contains information about schools, including their grade span (‘GSoffered‘) and longitude (‘Longitude‘)

  4. [12]

    **Identify the Requirement**: We need to find the school with the highest longitude value and then retrieve its grade span

  5. [13]

    - **LIMIT**: To get only the top result, which corresponds to the school with the highest longitude

    **Determine the SQL Operation**: - **ORDER BY**: To sort the schools by longitude in descending order. - **LIMIT**: To get only the top result, which corresponds to the school with the highest longitude. - **SELECT**: To specify that we want to retrieve the ‘GSoffered‘ column ...

  6. [14]

    In this context, it seems to be asking for the maximum absolute value of longitude, which could be either positive or negative

    ** distinguish between Absolute Longitude**: The question asks for the "highest" longitude, which could mean either the maximum or minimum longitude value. In this context, it seems to be asking for the maximum absolute value of longitude, which could be either positive or neg...

  7. [16]

    We need to find the molecules based on these conditions and order them alphabetically by ‘molecule_id‘

    **Identify the key requirements**: The question asks for the top three molecules that have single bonds between two atoms and are not carcinogenic. We need to find the molecules based on these conditions and order them alphabetically by ‘molecule_id‘

  8. [17]

    - ‘bond‘ table contains the ‘bond_type‘ of bonds and is linked to ‘molecule_id‘

    **Determine the relevant tables**: - ‘molecule‘ table contains the ‘ molecule_id‘ and ‘label‘ of molecules. - ‘bond‘ table contains the ‘bond_type‘ of bonds and is linked to ‘molecule_id‘

  9. [18]

    - We need to filter for bonds where ‘bond_type = ’-’‘ (single bond)

    **Filter conditions**: - We need to filter for molecules where ‘label = ’-’‘ (not carcinogenic). - We need to filter for bonds where ‘bond_type = ’-’‘ (single bond)

  10. [19]

    **Join the tables**: - Join ‘molecule‘ and ‘bond‘ tables on ‘molecule_id‘

  11. [20]

    **Order and limit**: - Order the results by ‘molecule_id‘ and limit the output to the top three

  12. [21]

    - Apply WHERE clauses to filter for not carcinogenic and single bonds

    **Formulate the SQL query**: - Use an INNER JOIN to combine ‘molecule‘ and ‘bond‘ tables. - Apply WHERE clauses to filter for not carcinogenic and single bonds. - Use ORDER BY to sort by ‘molecule_id‘. - Use LIMIT to restrict the output to 3 rows. </think> <answer> SELECT T1.m...

  13. [22]

    The ‘molecule‘ table contains the ‘label‘ information, the ‘bond‘ table contains the ‘bond_type‘ information, and the ‘connected‘ table links atoms to bonds and molecules

    **Identify the relevant tables**: We need to access information from the ‘molecule‘, ‘bond‘, and ‘connected‘ tables. The ‘molecule‘ table contains the ‘label‘ information, the ‘bond‘ table contains the ‘bond_type‘ information, and the ‘connected‘ table links atoms to bonds and...

  14. [23]

    **Filter molecules not carcinogenic**: We will use the ‘WHERE‘ clause to filter molecules with ‘label = ’-’‘ (not carcinogenic)

  15. [24]

    **Filter single type bonds**: We will also use a ‘WHERE‘ clause to filter bonds with ‘bond_type = ’-’‘ (single bonds)

  16. [25]

    **Group by molecule_id**: Since we want to list the top three molecules, we will use the ‘GROUP BY‘ clause to group the results by ‘molecule_id‘

  17. [26]

    Here’s how we can construct the SQL query: - Start with the ‘SELECT‘ statement to specify the required columns (‘molecule_id‘)

    **Order by molecule_id**: Finally, we will use the ‘ORDER BY‘ clause to sort the results in alphabetical order. Here’s how we can construct the SQL query: - Start with the ‘SELECT‘ statement to specify the required columns (‘molecule_id‘). - Use ‘FROM‘ and ‘INNER JOIN‘ to join...

  18. [27]

    Join the ‘molecule‘ and ‘bond‘ tables based on the ‘molecule_id‘ column

  19. [28]

    Filter the joined data to include only those rows where the ‘label‘ is ’-’ (indicating the molecule is not carcinogenic) and the ‘bond_type‘ is ’-’ (indicating a single bond)

  20. [29]

    Group the results by ‘molecule_id‘ to exclude duplicates in the result set

  21. [30]

    Order the results by ‘molecule_id‘ in ascending order

  22. [31]

    The draft SQL query 1 seems to have an issue; it’s not correctly filtering out duplicates and isn’t grouping the results, which would explain why it returns duplicate rows

    Limit the results to the top 3 rows. The draft SQL query 1 seems to have an issue; it’s not correctly filtering out duplicates and isn’t grouping the results, which would explain why it returns duplicate rows. The draft SQL query 2 avoids duplicates by grouping by ‘molecule_id...

  23. [32]

    Therefore, we need to look at the ‘satscores‘ table for SAT scores and the ‘schools‘ table for district information

    **Identify the relevant tables**: The question involves finding districts with high SAT scores and determining which one is active. Therefore, we need to look at the ‘satscores‘ table for SAT scores and the ‘schools‘ table for district information

  24. [33]

    **Join the tables**: Since the ‘satscores‘ table doesn’t directly contain district details, we need to join it with the ‘schools‘ table using the ‘CDSCode‘ column, which is the primary key in ‘schools‘ and acts as a foreign key in ‘satscores‘

  25. [34]

    **Filter for active districts**: We need to filter the results to include only active districts, which can be identified by the ‘StatusType‘ column in the ‘schools‘ table

  26. [35]

    **Calculate the average reading score**: We need to calculate the average reading score for each active district

  27. [36]

    **Order by average score**: We need to order the results by the average reading score in descending order to find the district with the highest average score

  28. [37]

    **Limit the results**: Since we are only interested in the district with the highest average score, we can limit the results to 1

  29. [39]

    Therefore, the relevant tables are ‘schools‘ and ‘satscores‘

    **Identify the relevant tables**: The question involves finding information about districts and their satscores. Therefore, the relevant tables are ‘schools‘ and ‘satscores‘

  30. [40]

    This means we can join these two tables on the ‘CDSCode‘ column

    **Understand the relationships**: The ‘schools‘ table has a foreign key ‘CDSCode‘ that references the ‘CDSCode‘ in the ‘satscores‘ table. This means we can join these two tables on the ‘CDSCode‘ column

  31. [41]

    Therefore, we need to add a filter in the ‘WHERE‘ clause to include only ‘StatusType = ’Active’‘ districts

    **Filter active districts**: The question specifies that we should only consider active districts. Therefore, we need to add a filter in the ‘WHERE‘ clause to include only ‘StatusType = ’Active’‘ districts

  32. [42]

    **Sort by average score in Reading**: To find the district with the highest average score in Reading, we need to sort the results in descending order based on the ‘AvgScrRead‘ column from the ‘satscores‘ table

  33. [43]

    **Limit the output**: Since we are interested in the district with the highest average score, we can limit the output to the first row

  34. [44]

    Which active district has the highest average score in Reading?

    **Select the required columns**: The question asks for the district name, so we need to select the ‘dname‘ column from the ‘satscores‘ table. Based on these steps, the SQL query can be written as follows: </think> <answer> SELECT T2.dname FROM schools AS T1 INNER JOIN satscore...

  35. [45]

    - ‘schools‘ table: contains the districts and their status

    **Identify the relevant tables and columns**: - ‘satscores‘ table: contains the average reading scores. - ‘schools‘ table: contains the districts and their status

  36. [46]

    **Join the tables**: - We need to join the ‘satscores‘ table with the ‘schools‘ table on the ‘cds‘ column to link each district with its average reading score

  37. [47]

    This can be done using a WHERE clause

    **Filter for active districts**: - We need to filter the results to include only active districts. This can be done using a WHERE clause

  38. [48]

    **Group by district**: - We need to group the results by district to calculate the average score for each district

  39. [49]

    **Order by average score in descending order**: - We need to sort the results by the average score in descending order to get the district with the highest score at the top

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    Which user ID has the lowest view?

    **Limit the results**: - Since we only want the district with the highest average score, we can limit the results to 1. According to the draft SQL provided, the steps seem to be followed correctly, but the table aliases in the query are not consistent. We will correct them to ...

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    The most relevant table for this information is the ‘users‘ table

    **Identify the relevant table**: The question is about users, their views, and their IDs. The most relevant table for this information is the ‘users‘ table

  42. [52]

    **Determine the relevant columns**: The columns of interest are ‘Views‘ and ‘Id‘ (user ID)

  43. [53]

    **Order the results by views**: To find the user with the lowest views, we need to order the results in ascending order based on the ‘Views‘ column

  44. [54]

    **Limit the results**: Since we are only interested in the user with the lowest views, we can limit the results to the first row of the ordered list

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    Which user ID has the lowest view?

    **Select the correct column**: The question asks for the user ID, so we need to select the ‘Id‘ column. Putting these steps together, we can formulate the SQL query. </think> <answer> SELECT Id FROM users ORDER BY Views ASC LIMIT 1 </answer> The top 1 group results after CSC v...

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    Identify the relevant table: The ‘users‘ table contains information about users, including their views

  47. [57]

    Determine the column of interest: The ‘Views‘ column in the ‘users‘ table contains the number of views each user has

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    Find the minimum value of views: To find the user with the lowest view, we need to identify the user with the minimum value in the ‘Views‘ column

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    The SQL query will use the ‘MIN‘ function to find the minimum number of views and then use a subquery to find the user ID with this minimum value

    Select the user ID: Once we have the minimum value, we select the user ID from the ‘users‘ table that corresponds to this minimum value. The SQL query will use the ‘MIN‘ function to find the minimum number of views and then use a subquery to find the user ID with this minimum ...

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    **Identify the required operation**: The task requires finding the user ID with the minimum view count

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    **Determine the appropriate SQL function**: The SQL function ‘MIN‘ can be used to find the minimum value in the ‘Views‘ column

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    - We use ‘ORDER BY Views ASC‘ to sort the rows in ascending order based on the view count

    **Formulate the SQL query**: - We need to select the ‘Id‘ from the ‘users‘ table. - We use ‘ORDER BY Views ASC‘ to sort the rows in ascending order based on the view count. - We use ‘LIMIT 1‘ to get only the top row, which corresponds to the user with the lowest view count

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    **Validate the query**: - The first draft SQL provided is: ‘SELECT Id FROM users ORDER BY Views ASC LIMIT 1‘ - The execution result confirms that this query correctly returns the user ID with the lowest view count. Based on these steps, the final SQL query is: ‘SELECT Id FROM ...

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    {PREDICT_SQL1} Execution result {EXECUTE_RESULT1}

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    If the question asks for a specific column, make sure to only include that column in the SELECT clause, nothing more

    {PREDICT_SQL2} Execution result {EXECUTE_RESULT2} Instructions: - Make sure you only output the information that is asked in the question. If the question asks for a specific column, make sure to only include that column in the SELECT clause, nothing more. - The generated quer...

  56. [2023]

    arXiv preprint

    C3: Zero-shot Text-to-SQL with ChatGPT. arXiv preprint. ArXiv:2307.07306 [cs] version: 1. Dawei Gao, Haibin Wang, Yaliang Li, Xiuyu Sun, Yichen Qian, Bolin Ding, and Jingren Zhou. 2023. Text-to-SQL Empowered by Large Language Mod- els: A Benchmark Evaluation. arXiv preprint . ...

    2023 arXiv

  57. [2025]

    Preprint, arXiv:2501.12948

    Deepseek-r1: Incentivizing reasoning capa- bility in llms via reinforcement learning. Preprint, arXiv:2501.12948. Xuemei Dong, Chao Zhang, Yuhang Ge, Yuren Mao, Yunjun Gao, lu Chen, Jinshu Lin, and Dongfang Lou

    arXiv

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