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REVIEW 3 major objections 5 minor 7 cited by

Interpretable LLM-based Table Question Answering

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Plan-of-SQLs makes LLM table question answering interpretable by decomposing each question into atomic natural-language steps, each translated into an executable SQL command, so every intermediate result is transparent.

desk verdict A genuinely useful interpretable Table QA method with strong human-simulation evidence and a real efficiency win, but the paper overclaims a 100% faithfulness guarantee that its own fallback path contradicts. read the letter →

arxiv 2412.12386 v3 pith:VR3OJ44M submitted 2024-12-16 cs.CL cs.LG

Giang Nguyen , Ivan Brugere , Shubham Sharma , Sanjay Kariyappa , Anh Totti Nguyen , Freddy Lecue This is my paper · ORCID
classification cs.CLcs.LG
keywords TableQuestionAnsweringInterpretabilitySQLLargeLanguageModelsPlan-of-SQLsExplainableAIForwardsimulationAttributionmaps
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

Plan-of-SQLs (POS) is a Table Question Answering method built around a simple idea: instead of asking an LLM to answer a question about a table in one opaque step, decompose the question into a short sequence of atomic natural-language steps, translate each step into one executable SQL command, and run them in order. Every intermediate table is therefore a visible artifact, and the final answer is produced by deterministic SQL rather than by hidden LLM reasoning. The paper claims that these step-by-step, SQL-grounded explanations help users simulate and verify the model's decisions better than explanations from Text-to-SQL, DATER, or CoTable, while QA accuracy stays within one or two points of the best hybrid methods. It also reports large efficiency gains (four LLM calls and two database queries per question, versus up to 100 calls and 50 queries for baselines) and stable accuracy on large tables, where baselines degrade. The main caveat the paper itself identifies is that the planner's step sequence is the bottleneck: planning errors account for 70.7–95.4% of POS failures.

What carries the argument

The load-bearing mechanism is the atomic step, defined as a minimal natural-language table operation restricted to at most one condition and one variable (for example, "Select rows where opponents = 0"), which the Step-to-SQL module translates into a single SQL statement (SQL being the standard language for querying relational tables). The NL Atomic Planner produces the step sequence; each SQL executes on the current intermediate table, and an attribution-map generator marks the rows, columns, and cells used at every step, producing a chain of highlighted intermediate tables. This chain is the explanation: it shows which data contributed to the answer, when, and through which operation.

What would settle it

Run the paper's own ablation on a fresh, larger set of non-expert users: keep the natural-language plan but replace SQL execution with direct black-box LLM table transformations, then measure verification accuracy. The paper's claim that SQL grounding is the key interpretability component predicts a clear drop; if users verify just as well without executed SQL, the central claim would fail.

Watch

Extended reading notes

Core claim

The paper's central claim is that interpretability in Table QA can be achieved by making the reasoning process literally identical to the computation: each atomic step is a natural-language operation with at most one condition and one variable, converted into a single SQL statement and executed on the current table. Because the explanation is the executed trace, POS explanations are faithful and comprehensive by construction, with no separate reasoning to explain. On TabFact, WikiTQ, and FeTaQA, POS achieves accuracy competitive with existing methods (within 1–2 points), while its explanations rank first in preference, simulation, and verification studies with both human and LLM judges, and it requires up to 25x fewer LLM calls and database queries than baselines. The paper is careful to note that this full transparency applies to the SQL-executed steps; for free-form FeTaQA answers, the final narrative generation is delegated to an LLM, so those explanations are not claimed to be 100% faithful.

Load-bearing premise

The whole method assumes the LLM planner will write a correct sequence of atomic steps; the paper's own error analysis shows planning mistakes cause 70.7–95.4% of failures, so if the planner is weak, both accuracy and explanation quality collapse.

Editorial extensions

If this is right

  • If POS is right, explanation quality in Table QA does not have to be traded against accuracy: users get both within one or two accuracy points of state-of-the-art hybrid methods.
  • The up-to-25x reduction in LLM calls and database queries means POS is substantially cheaper to deploy at scale than DATER, Binder, or CoTable.
  • Because POS keeps accuracy stable across table sizes while baselines drop by 30–50 points on large tables, it is a stronger candidate for real-world tables that exceed LLM context limits.
  • The high agreement between LLM and human judges (up to 90.59% in forward simulation) suggests that LLM judges can be used as proxies in early-phase explanation evaluation.
  • For free-form answers, POS's intermediate SQL steps still improve BLEU and ROUGE over end-to-end and few-shot QA, indicating the decomposition helps even when a final LLM writing step is required.

Reading between the lines

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

  • Because every reasoning step is an executable SQL statement, one could build an automated verifier that re-executes each step and checks the final answer against the plan, turning explanation quality from a subjective rating into a formal property; the paper sketches error detection but does not build this.
  • The paper measures explanation quality on semi-structured benchmark tables; an untested corollary is that POS's atomicity should make it relatively robust to messy real-world tables once a normalization preprocessor is added, which the authors suggest as future work.
  • The 70.7–95.4% planning-error share implies that improvements in LLM planning ability should translate almost directly into POS accuracy gains, so POS is a planner-limited rather than an executor-limited architecture; this is an interpretation consistent with the paper's own error analysis.
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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 / 5 minor

Summary. The paper proposes Plan-of-SQLs (POS), a Table QA method that decomposes a question into atomic natural-language steps, translates each step into an executable SQL command, and executes the commands sequentially so that intermediate tables and the final answer are produced programmatically. The authors evaluate explanation quality through preference ranking, forward simulation, and prediction verification with both human and LLM judges, and evaluate QA accuracy and efficiency on TabFact, WikiTQ, and FeTaQA. The central claims are that POS produces the highest-quality explanations among compared methods, maintains competitive QA accuracy with far fewer LLM calls and database queries, scales robustly to large tables, and that LLM judges agree strongly with human judges in explanation evaluation.

Significance. If the claims hold, POS is a genuinely useful contribution: it is a simple, reproducible, program-grounded Table QA design whose explanations can be inspected cell-by-cell, it comes with released code and data, and it is evaluated with behavioral XAI tasks (forward simulation and verification) rather than subjective ratings alone. The efficiency result (4 LLM calls vs. up to 100 for DATER) and the ablation isolating SQL execution as the main driver of interpretability are valuable and clearly presented. The main reservations are that the absolute faithfulness guarantee is contradicted by the paper's own fallback path and FeTaQA extension, and that some interpretability evidence rests on prompt asymmetries and small human samples without confidence intervals.

major comments (3)
  1. [Section 5 and Appendix C vs. Section 2.3 and Section 4.1.3] The paper's absolute-faithfulness claim is load-bearing and internally inconsistent. Section 2.3 states that POS 'guarantees 100% faithfulness and comprehensiveness' because explanations represent reasoning through executed SQL operations, and Section 4.1.3 uses this 'absolute faithfulness' to explain why POS beats post-hoc self-explanation. However, Section 5 reports a fallback path in which, if SQL execution fails, an LLM answers end-to-end, and this path is triggered in 3.16% of TabFact and 13.58% of WikiTQ samples; Table 6 reports accuracy including this fallback. Appendix C further states that for FeTaQA the final answer 'is generated through a black-box operation of LLMs—making POS explanations no longer 100% faithful or comprehensive,' yet Section 4.4 presents FeTaQA results as part of POS. The guarantee therefore does not hold for the full reported system. The authors should rescope the faithfulness/comprehensiveness claim to the executed-SQL subset, report interpretability metrics separately for fallback and non-fallback samples, and soften the abstract and Section 2.3 wording accordingly.
  2. [Appendix G.3 and Table 1(a)] The LLM-as-judge preference-ranking prompt is not symmetric across methods, so Table 1(a) is confounded. In Appendix G.3, the POS description lists three components and explicitly advertises attribution maps and cell highlighting, while the Text2SQL description says only that the generated SQL command serves as the explanation, and DATER receives a shorter two-item description. Since the judge is asked to rank on 'clarity, coherence, and helpfulness,' the richer textual description can itself produce the observed POS advantage (average ranks 1.01–1.55). The authors should rerun the preference ranking with equal-length, same-structure method descriptions, or supplement it with human preference rankings; otherwise the 'highest-quality explanations' claim rests on a prompt artifact.
  3. [Section 4.1.1 and Table 1(b)] The human forward-simulation result is the strongest direct evidence for the interpretability claim, but it is reported without confidence intervals, significance tests, or participant-level variation. With 32 volunteer CS students and roughly 200 responses per method, the reported 93.00% for POS versus 83.68–86.50% for baselines could still reflect sampling variability, and the abstract's claim that POS 'markedly improves' users' ability to simulate decisions needs statistical support. A permutation test or clustered bootstrap over participants is needed. In addition, Table 4 reports 'Human Samples' of 153, 172, 177, and 171 per method, which does not match the stated 800 total responses, so the response-count accounting should be clarified.
minor comments (5)
  1. [Appendix A] In the Text-to-SQL paragraph of Appendix A, the sentence 'Therefore, the we recruited Computer Science students (who have SQL expertise) for our human study' contains a duplicated article and should read 'Therefore, we recruited...'.
  2. [Table 10 and Section 4.1.3] Table 10 reports a preference value of 5.00 for 'Self-explanation' while the four main methods are ranked on a 1-to-4 scale; the scale and the number of alternatives used for this table should be defined explicitly.
  3. [Section 5] The claim that every table-based question can be decomposed into atomic SQL steps because 'SQL is relationally complete' is too strong for the implemented system, which operates on a single local table and cannot access external or world knowledge, as the paper itself concedes later in Appendix C. A qualifying sentence would remove the apparent contradiction.
  4. [Appendix G.3] The HTML snippet for cell highlighting uses 'background-color:90EE90' without a leading '#'; the intended value is presumably '#90EE90'.
  5. [Table 7] For the table-size analysis, the paper compares against degradation numbers 'from Wang et al. (2024)' but only cites the source in text; please provide the exact table or numbers in the appendix so the comparison is verifiable.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: accuracy and interpretability claims are anchored to external benchmarks and human judgments; the overstated faithfulness guarantee is a scoping inconsistency, not a circular reduction.

full rationale

The paper contains no fitted parameters, no formal derivation, and no prediction that reduces by construction to its inputs. POS accuracy is measured on standard external benchmarks (TabFact, WikiTQ, FeTaQA) against published baselines, and the efficiency gains are direct counts of LLM calls and database queries. The interpretability claims are supported by human studies (e.g., 93.00% forward-simulation accuracy for POS) and by LLM-judge evaluations whose headline agreement with humans (up to 90.59%) is measured, not assumed. The overlap between the GPT-family models used for planning and judging is a possible experimental confound, but it is not a by-construction circularity because the central interpretability result is independently anchored to direct human judgments. The absolute faithfulness guarantee in Sections 2.3 and 4.1.3 is definitional for the SQL-executed path, and the paper itself acknowledges the scope limits in Section 5 (fallback on 3.16% of TabFact and 13.58% of WikiTQ samples) and Appendix C (FeTaQA answers are generated through a black-box LLM operation, so POS explanations are no longer 100% faithful there). That is an internal consistency and scoping problem, not a circular derivation: on the non-fallback subset the explanation is literally produced from the executed SQL trace. No load-bearing self-citation is present; citations to the authors' earlier work are for generic evaluation protocols, not for the central claim. Under the required standard of exhibiting a specific reduction, no circular step is established.

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

The paper introduces no fitted numeric parameters; its claims are empirical and depend on LLM behavior assumptions. The key assumptions are that SQL relational completeness makes atomic decomposition universal (contradicted by the paper's own exceptions), that LLM judges are unbiased proxies for humans, that Step-to-SQL is faithful, and that CS students proxy for real users. The fallback path means the faithfulness assumption fails for a nontrivial fraction of samples.

assumptions (4)
  • domain assumption Relational completeness of SQL implies any table question can be decomposed into atomic steps.
    Used in Section 5 to justify the claim that POS can handle any table question; Appendix C itself lists free-form narrative and world-knowledge questions as exceptions, so the axiom is not valid in full generality.
  • domain assumption LLM judges are reliable proxies for human users of explanations.
    The paper relies on prior LLM-judge papers and its own correlation and agreement analysis, but the agreement data covers one dataset (TabFact) and three LLM families, and the preference prompt describes methods with unequal detail.
  • domain assumption The Step-to-SQL module translates each natural-language step into an SQL query that faithfully implements that step.
    The paper claims in Appendix J.2 that no Step-to-SQL instance produced an incorrect SQL query, but this is a self-reported analysis on the paper's own evaluation set, not an independent guarantee; fallback execution failure also contradicts universal faithfulness.
  • domain assumption Human participants (CS students) represent relevant users of Table QA explanations.
    Section 4.1.1 recruits 32 CS students; results may not generalize to non-programmer domain experts or broader user populations.

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Cite this review

Pith. "Pith review of Interpretable LLM-based Table Question Answering." pith.science (2026). https://pith.science/paper/VR3OJ44M

@misc{pith2026241212386,
  author       = {Pith},
  title        = {Pith review of: Interpretable LLM-based Table Question Answering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VR3OJ44M}},
  note         = {Machine review of arXiv:2412.12386}
}
read the original abstract

Interpretability in Table Question Answering (Table QA) is critical, especially in high-stakes domains like finance and healthcare. While recent Table QA approaches based on Large Language Models (LLMs) achieve high accuracy, they often produce ambiguous explanations of how answers are derived. We propose Plan-of-SQLs (POS), a new Table QA method that makes the model's decision-making process interpretable. POS decomposes a question into a sequence of atomic steps, each directly translated into an executable SQL command on the table, thereby ensuring that every intermediate result is transparent. Through extensive experiments, we show that: First, POS generates the highest-quality explanations among compared methods, which markedly improves the users' ability to simulate and verify the model's decisions. Second, when evaluated on standard Table QA benchmarks (TabFact, WikiTQ, and FeTaQA), POS achieves QA accuracy that is competitive to existing methods, while also offering greater efficiency-requiring significantly fewer LLM calls and table database queries (up to 25x fewer)-and more robust performance on large-sized tables. Finally, we observe high agreement (up to 90.59% in forward simulation) between LLMs and human users when making decisions based on the same explanations, suggesting that LLMs could serve as an effective proxy for humans in evaluating Table QA explanations.

Figures

Figures reproduced from arXiv: 2412.12386 by the authors.

Figure 1
Figure 1. (a) End-to-End: relies entirely on an LLM to answer the question directly, leaving no room for users to understand the prediction. (b) Text-to-SQL: generates an SQL command to solve the question, requiring domain expertise to understand and becoming unintelligible when the question becomes complex. (c) Chain-of-Table or CoTable: performs planning with abstract functions and executes sequentially to arrive at the fin… view at source ↗
Figure 2
Figure 2. Illustration of Plan-of-SQLs (POS). 1 The Natural Language (NL) Atomic Planner takes (T, Q) as input and generates a step-by-step plan in plain language. 2 Step-to-SQL then takes (T, S1) and converts the first step S1 into an SQL query, which is executed on T to produce an intermediate table T1. 3 Step-to-SQL takes (T1, S2) to produce and execute the next SQL query, yielding T2. 4 The final Step-to-SQL uses (T2, S3)… view at source ↗
Figure 3
Figure 3. Generating attributions maps for POS. Column idx is added to track row attribution. A.1 Attribution Maps During the execution of each SQL command, we perform the following steps: • Adding the tracking index column: Before executing an SQL, we add a tracking index column to the current table. This column contains the original row indices from the initial table— [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗
Figures from the paper (16 more)
Figure 4
Figure 4. Figure 4: Text-to-SQL explanations provide only the SQL command, which is intuitive for SQL users. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_4.png]
Figure 5
Figure 5. Figure 5: DATER explanations contain sub-table selection, contextual information (or verified facts), and highlights that reveal which input features influence the final answer. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_5.png]
Figure 6
Figure 6. Figure 6: CoTable explanations present intermediate tables and highlights, showing key steps in data transformation. Additionally, the steps are presented through function names and their arguments. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_6.png]
Figure 7
Figure 7. Figure 7: POS explanations contain intermediate tables and highlights. The green-highlighted cells indicate where the information in the table matches the conditions specified in the natural language steps. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_7.png]
Figure 8
Figure 8. Figure 8: An example of a post-hoc self-explanation. The LLM first produces an answer (TRUE) and then [PITH_FULL_IMAGE:figures/full_fig_p026_8.png]
Figure 9
Figure 9. Figure 9: Upper: POS explanation with atomicity. Lower: POS explanation without atomicity. 30 [PITH_FULL_IMAGE:figures/full_fig_p030_9.png]
Figure 10
Figure 10. Figure 10: Two POS explanations without atomicity. The steps are compound and the attribution maps are non-trivial to comprehend. 31 [PITH_FULL_IMAGE:figures/full_fig_p031_10.png]
Figure 11
Figure 11. Figure 11: Data-attribution tracking algorithm for POS. E.1 Algorithm Overview The algorithm follows these main steps: 1. Preprocessing: Remove comments and normalize whitespace in the SQL query. 2. Column Extraction: Parse different clauses of the SQL query to identify column n…
Figure 12
Figure 12. Figure 12: Although CoTable correctly answers the question [PITH_FULL_IMAGE:figures/full_fig_p033_12.png]
Figure 13
Figure 13. Figure 13: DATER selects rows 2, 3, 4, 5, and 9 to answer the question. However, the inclusion of row 3 is [PITH_FULL_IMAGE:figures/full_fig_p034_13.png]
Figure 14
Figure 14. Figure 14: POS predicts TRUE but the groundtruth is FALSE (False Positive). In planning, POS misses checking the player name. 42 [PITH_FULL_IMAGE:figures/full_fig_p042_14.png]
Figure 15
Figure 15. Figure 15: POS predicts TRUE but the groundtruth is FALSE (False Positive). In planning, POS misses checking the player name. 43 [PITH_FULL_IMAGE:figures/full_fig_p043_15.png]
Figure 16
Figure 16. Figure 16: POS predicts TRUE but the groundtruth is FALSE (False Positive). In planning, POS misses checking the score. 44 [PITH_FULL_IMAGE:figures/full_fig_p044_16.png]
Figure 17
Figure 17. Figure 17: POS predicts FALSE but the groundtruth is TRUE (False Negative). In planning, POS misses checking the year. 45 [PITH_FULL_IMAGE:figures/full_fig_p045_17.png]
Figure 18
Figure 18. Figure 18: POS predicts FALSE but the groundtruth is TRUE (False Negative). In planning, POS should select two rows at the same step. 46 [PITH_FULL_IMAGE:figures/full_fig_p046_18.png]
Figure 19
Figure 19. Figure 19: POS predicts FALSE but the groundtruth is TRUE (False Negative). Arguably, this error can be attributed to the planner, as it sees both the query and the input table and should therefore generate the correct step (i.e., Select rows where ‘player’ is ‘thomas bjarn’). W…

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Forward citations

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Reference graph

Works this paper leans on

26 extracted references · 25 canonical work pages · cited by 7 Pith papers

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    Verify if the Statement if True or False agaisnt the Input Table

    work page

  2. [2]

    Explain your verification process based on the provided info

    work page

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    the wildcats kept the opposing team scoreless in four games

    Your answer must be TRUE or FALSE only. Statement: the wildcats kept the opposing team scoreless in four games Input Table Caption:1947 kentucky wildcats football team game date opponent result wildcats_points opponents record 1 9999-09-20 ole miss loss 7 14 0 - 1 2 9999-09-27 cincinnati win 20 0 1 - 1 3 9999-10-04 xavier win 20 7 2 - 1 4 9999-10-11 9 geo...

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    Use a CASE statement to return TRUE if the number of rows is equal to 1, otherwise return FALSE. H.2 Prompt for Step-to-SQL /usrPrompt for Step-to-SQL [In-context examples] Given this table: /* {table2string(intermediate_table)} */ Data types of columns: • {col_1}: {dtype_str_1} • {col_2}: {dtype_str_2} • . . . Write an SQL command that: {natural_language...

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    Preprocessing: Remove comments and normalize whitespace in the SQL query

    work page

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    • WHERE clause: Identify columns used in conditions

    Column Extraction: Parse different clauses of the SQL query to identify column names: • SELECT clause: Extract both regular columns and those used in functions. • WHERE clause: Identify columns used in conditions. • ORDER BY clause: Extract columns used for sorting

    work page

  7. [7]

    "" G.2 Prompt to LLM Judges in Forward Simulation /usrPrompt for LLM-as-a-Judge in Forward Simulation prompt = f

    Filtering: Compare extracted columns against a list of original columns to ensure validity. E.2 Implementation Details The algorithm is implemented using regular expressions to parse the SQL query. Key implementation details include: • Use of re.sub() for comment removal and whitespace normalization. • Application of re.search() and re.findall() for extra...

    work page 2025

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    Sub-table Selection: DATER selects a sub-table from the original input Table that is relevant to the Statement

    work page

Show all 26 references
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    "", "COT

    Contextual Information: DATER provides contextual information that is fact-checked against the Table.""", "COT": """COT is a method that breaks down the question-answering process into a series of intermediate tables. Each step in the chain represents a specific operation on t...

  2. [10]

    Step Descriptions: Each step is accompanied by a function with arguments, providing context for the transformation

  3. [11]

    Intermediate Tables: We display the intermediate tables resulting from each function, showing the state of the data at each step

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    "", "Text2SQL

    Row and Column Highlighting: Rows and Columns used in the current step are highlighted with background- color:yellow.""", "Text2SQL": """Text2SQL is a method that translates the natural language question into a single SQL query. The SQL query itself serves as the explanation f...

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    Step Descriptions: Each step is accompanied by a natural-language description of the atomic step performed, providing context for the transformation

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    Intermediate Tables: We display the intermediate tables resulting from each step, showing the state of the data at each step

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    "" } for i in range(num_methods): shuffled_methods = methods[i:] + methods[:i] prompt = f

    Attribution Maps: We highlight the the rows, columns, and cells involved in each table transformation over intermediate tables. Row and Column Highlighting: Rows and Columns used in the current step are highlighted with background- color:yellow. Cell Highlighting: Cells that d...

    2025

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    Order the table by ‘rank’ in ascending order

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    Select rows where ‘athlete’ is ‘manjeet’ using the LIKE function

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    If adding new columns, they should be different than columns {existing_cols}

    If using SELECT COUNT(*), SUM, MAX, A VG, you MUST use AS to name the new column. If adding new columns, they should be different than columns {existing_cols}

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    Your SQL command MUST be compatible and executable by Python sqlite3 and pandas

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    If using FROM, the table to be selected MUST be {table_name}. 38 Published in Transactions on Machine Learning Research (06/2025) I In-context Examples I.1 In-context Examples for Atomic Planning /usrIn-context examples for atomic planning TabFact Table: 2005 tournament result...

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    Select rows where the ‘name’ is ‘bob’ or ‘charlie’

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    Select rows where ‘hometown’ is ‘chicago’

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    Use a CASE statement to return TRUE if the number of rows is equal to 2, otherwise return FALSE. WikiTQ Table: 2005 tournament results /* col : id | name | hometown | score row 1 : 1 | alice | new york | 85 row 2 : 2 | bob | los angeles | 90 row 3 : 3 | charlie | chicago | 75 ...

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    Select rows where the ‘hometown’ is ‘chicago’

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    bjørn” when it generates its first step (“Select rows where player = ‘thomas bjørn’

    Select the ‘name’ column. 39 Published in Transactions on Machine Learning Research (06/2025) I.2 In-context Examples for Step-to-SQL /usrIn-context examples for Step-to-SQL Given this table: /* col : id | name | department | salary | years row 1 : 1 | alice | it | 95000 | 3 r...

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    Select rows

    Springer, 2022. Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, and George Karypis. Opentab: Advancing large language models as open-domain table reasoners. In The Twelfth International Conference on Learning...

    2022 arXiv

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