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REVIEW 3 major objections 5 minor 55 references

Text to Query Plans for Question Answering on Large Tables

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read TSO turns natural-language questions into step-by-step query plans, handling tables with 8,058 columns.

desk verdict The large-table claim rests on a retrieval algorithm whose column-level fallback is dead code, so the paper's central result is unsupported as written—though the architecture is plausible and the problem is real. read the letter →

arxiv 2508.18758 v1 pith:MDBFBLWB submitted 2025-08-26 cs.DB cs.AIcs.CL

Yipeng Zhang , Chen Wang , Yuzhe Zhang , Jacky Jiang This is my paper
classification cs.DBcs.AIcs.CL
keywords text-to-query-planlargetablesReActvectorretrievaltablequestionansweringLLMdataframeoperationsSQLalternative
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 claims that natural-language querying of very large tables can be done without SQL and without fitting the table into an LLM's context window. The proposed TSO system has a language model pick one dataframe operation at a time—filter, join, aggregate, even PCA—building a tree of intermediate results until it reaches the answer. To keep the model from drowning in schemas with thousands of columns, TSO builds a three-level index of LLM-generated descriptions of columns, clusters, and tables, and retrieves only the columns judged relevant to the user's question. On the Spider benchmark, TSO with GPT-4o reaches 70.34% execution-based exact match, close behind DIN-SQL's 74.20%, and on a 266,033-row, 8,058-column agronomic dataset it answers 16 of 20 self-designed questions. If the approach holds, it gives non-programmers a way to query and analyze scientific and business tables that are currently too wide for other LLM-based methods.

What carries the argument

The tree-structured plan, in which raw tables are leaves, intermediate DataFrames are internal nodes, and the answer is the root; and the three-level vector index (tables, column clusters, columns) whose LLM-generated descriptions are embedded and matched against the query with 0.75 similarity thresholds. The tree linearizes into a step-by-step plan so the ReAct-style loop can build it incrementally and backtrack, while the vector index decides which columns the model ever sees.

What would settle it

Run a query over a wide table where a relevant column has an inaccurate LLM-generated description (as happened with Question 7 on the agronomic dataset, where 'one year' became 'one day') and show that the retriever omits the column, making TSO's final answer incomplete. A systematic version: compute the recall of relevant columns over a suite of queries; if recall is below 100%, the large-table capability is not reliable.

Watch

Extended reading notes

Core claim

The central discovery is that a natural-language question can be converted directly into a tree-structured plan of dataframe operations—rather than into SQL—by an LLM that picks one operation at a time. For schemas with thousands of columns, a three-level vector index built from LLM-generated descriptions of columns, their clusters, and tables retrieves only the columns relevant to the query, bypassing the model's context limit. The paper reports that this system reaches 70.34% execution-based exact match on Spider-dev (vs. DIN-SQL's 74.20%) and answers 16 of 20 questions on an 8,058-column agronomic dataset, while also supporting operations like PCA and anomaly detection.

Load-bearing premise

The large-table results rest on one premise: the automatically generated descriptions of columns, clusters, and tables are faithful enough that the similarity thresholds (0.75) plus LLM validation retrieve every column a correct plan needs; if one relevant column is dropped, the answer is wrong and the planner cannot recover.

Editorial extensions

If this is right

  • TSO matches the accuracy of a leading text-to-SQL system on Spider-dev (70.34% vs. 74.20% EM) without training on the benchmark, while also supporting analytical operations SQL cannot express.
  • Tables with thousands of columns—like the 8,058-column agronomic dataset—can be queried in natural language, and TSO answered 16 of 20 self-designed questions on it.
  • Because the planner works on DataFrames outside a database engine, it avoids SQL's scaling limits and can run on CSV or spreadsheet data.
  • The tree structure allows backtracking, so an LLM can recover from a wrong intermediate step instead of committing to a full SQL statement.
  • Performance scales with the underlying LLM: GPT-4o greatly outperforms GPT-4o-mini and Llama 3.1, so gains from better models transfer directly.

Reading between the lines

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

  • A standardized benchmark for 'too-wide' schemas, with ground-truth column relevance, would test whether the 16/20 result generalizes; the paper's 20 self-designed queries are a first step, not a yardstick.
  • The retrieval bottleneck suggests that improving column-description fidelity—for example, grounding descriptions in sample values or column-name heuristics—might matter more than the planner itself for real-world tables.
  • The same three-level retrieval could be bolted onto text-to-SQL systems, shrinking the schema to only relevant columns before SQL generation, potentially helping them on large-schema benchmarks.
  • An interactive implication: since descriptions are built once per schema, users could iteratively refine queries against a large dataset without re-embedding.
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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 TSO, an LLM-driven ReAct agent that converts natural language questions into executable query plans over DataFrames instead of generating SQL. It introduces a tree-structured plan representation and a three-level column/cluster/table vector index to scale to schemas with thousands of columns. Experiments are reported on Spider-dev and on a 266,033-row, 8,058-column agronomic dataset. With GPT-4o, TSO achieves 70.34% on the authors' exact-match metric versus 74.20% for a DIN-SQL variant, and it answers 16 of 20 self-designed agronomic queries. The central claim is that TSO can handle large tables and complex analytical tasks that exceed SQL and LLM context limits.

Significance. The idea of planning over tabular data with an iteratively built query plan, rather than generating SQL, is timely and potentially useful for scientific datasets with very wide schemas. The hierarchical retrieval design addresses a real scalability bottleneck. However, the evidence presented is not yet sufficient: the Spider comparison uses a nonstandard metric, the large-table evaluation is entirely self-designed, and Algorithm 2 contains a control-flow inconsistency that would make column-level retrieval unreachable. The paper is honest in reporting four failure cases, but those cases also expose the fragility of the retrieval assumption. If the methodological issues are fixed, the approach could be a meaningful contribution; in its current form, the central claims are not established.

major comments (3)
  1. [Algorithm 2, Section 3.5] Lines 12-22 contain a control-flow error that makes column-level fallback unreachable. The `else` on line 16 is paired with the inner `if C* = yes` (line 14), not with the outer validation on line 12. If cluster validation succeeds, C* is always 'yes' and the else is dead code; if cluster validation fails, the whole block is skipped. This contradicts the prose in §3.5: 'otherwise, the algorithm evaluates the relevance of individual columns.' Since clusters over 8,058 columns will rarely be perfectly homogeneous, this bug would cause relevant columns to be missed. The pseudocode must be corrected, and the implementation must be checked against the corrected logic.
  2. [Section 4.2, Table 1] The Spider evaluation uses a self-defined EM metric: execute the gold SQL and compare its result with TSO's DataFrame. This is not the official Spider evaluation protocol, and no evidence is given that the cited DIN-SQL numbers were obtained under the same comparison. Table 1 also lists 'DIN-SQL1' and 'DIN-SQL2' without defining the difference, and the 'second-best' claim refers only to the three rows shown, not to the Spider leaderboard. Without re-running baselines under the same metric or using the official Spider evaluation script, the comparison is not valid.
  3. [Section 4.3, Table 2] The large-table claim rests entirely on 20 self-designed queries on one dataset, with manually obtained ground truth and a hand-set 10% tolerance for prediction tasks. There is no baseline, no ablation for the retrieval thresholds θ_t=θ_c=θ_l=0.75, and no specification of the clustering algorithm or cluster count used in Algorithm 1. Question 7's failure further shows that a single LLM description error causes the retriever to miss a relevant column. This evidence is too weak to support the conclusion that TSO 'is capable of handling super large tabular data under complex Table QA tasks.'
minor comments (5)
  1. [Appendix A.1] The NP-hardness proof is only a sketch. Classical planning is PSPACE-complete in general; it is NP-complete only with a polynomial plan-length bound. The reduction from planning to the Table QA problem is asserted, not constructed. Please rephrase or prove a bounded variant.
  2. [Table 1] The reported numbers have no variance. Since LLM-based planning is stochastic, please report multiple runs or state that decoding was made deterministic and how.
  3. [Section 4.4] The seven ground-truth-error examples are anecdotal. They do not quantify how many of TSO's 29.66% errors on Spider are attributable to benchmark issues, so the claim that such issues 'significantly impact' the evaluation is not supported.
  4. [Algorithm 1, line 7] The clustering method and the number of clusters are not specified. These are free parameters that directly affect retrieval quality and should be described for reproducibility.
  5. [General] There are several typos and formatting issues: 'METHDOLOGY' in the Section 3 heading, a duplicated sentence in Section 1 ('While some efforts have attempted...'), and 'Table-Colum Retriever' in Figure 2.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; derivation is self-contained despite evaluation weaknesses.

full rationale

The main claimed derivation—transforming natural language queries into executable query plans via iterative ReAct reasoning and three-level vector retrieval—does not reduce to its own inputs. The NP-hardness argument (Appendix A.1) is a standard polynomial-time reduction from classical planning, not a circular appeal. The Spider evaluation compares TSO outputs against ground-truth SQL execution on an external benchmark (Spider-dev) and against an external strong baseline (DIN-SQL), so the central performance claim has independent grounding. No load-bearing self-citation appears: references [1] and [20] for tree/sequence equivalence are classic external works, and no uniqueness theorem or ansatz is imported from the authors' prior work. The agronomic experiment is self-designed (Section 4.2, Table 2) and therefore provides weaker evidence for scalability, but it is not a circular prediction: the 20 queries and manual ground truth were constructed independently of the system's output, and the paper explicitly reports four failures (Q7, Q15, Q19, Q20 in Section 4.3), which would not occur if success were forced by construction. The reviewer's concern about Algorithm 2 (lines 12-16), where column-level fallback appears unreachable due to control-flow pairing, is an internal correctness/implementation inconsistency, not a circular reduction. The paper's own limitation discussion—misleading column descriptions, ambiguous queries, and agent debugging loops—further indicates the evaluation is not tautological. Therefore no circular step can be exhibited under the required standard.

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

The system adds no invented entities. The main free parameters are the three retrieval thresholds, the 10% prediction tolerance, and the undisclosed iteration limit and clustering configuration. The axioms are dominated by empirical premises: that LLM-written descriptions preserve column semantics well enough for embedding retrieval (falsified in one reported case), that the ReAct supervisor reliably picks the next correct operation, and that a stated mapping in Appendix A.1 constitutes a planning reduction.

free parameters (4)
  • similarity thresholds theta_t, theta_c, theta_l = 0.75 each
    Appendix A.4: hand-set thresholds for table, cluster, and column retrieval in Algorithm 2; no sensitivity analysis, directly controls which columns reach the planner.
  • prediction tolerance = 10% percentage error
    Section 4.3: a prediction counts correct if percentage error is within 10%; hand-chosen criterion for the agronomic benchmark.
  • maximum iteration limit = not disclosed
    Section 4.3, Questions 19 and 20: the agent 'became trapped in debugging code errors and was unable to find a solution within the maximum iteration limit'; the limit value and policy are not reported, yet they determine reported failures.
  • column clustering method and cluster count = not specified
    Algorithm 1, line 7 clusters columns by embeddings without naming the algorithm or the number of clusters; this affects cluster-level retrieval granularity.
assumptions (5)
  • ad hoc to paper Classical Planning is NP-complete, and the stated mapping D to s0, O to A, q to sg is a polynomial reduction.
    Appendix A.1 asserts the reduction in two sentences; no instances, encodings, or correctness argument are given, so the hardness claim is stated rather than established. The hardness result motivates the heuristic but is not otherwise load-bearing.
  • domain assumption Tree-structured plans and linear operation sequences are interchangeable without loss of information.
    Section 3.3 cites [1] (compiler textbook) and [20] (SHOP planner) for bidirectional transformability; this is a standard planning and computation-graph premise.
  • ad hoc to paper LLM-generated column, cluster, and table descriptions preserve the semantics needed to retrieve query-relevant columns.
    Section 3.5, Algorithms 1 and 2: the whole large-table capability depends on this; Question 7 in Section 4.3 is a direct counterexample showing a misleading description caused a retrieval miss.
  • domain assumption The supervisor LLM, given current tree state and retrieved columns, reliably chooses the correct next operation.
    Section 3.4 ReAct loop: correctness of the system is contingent on per-step action selection quality; no ablation isolates this.
  • domain assumption DataFrame operations (join, filter, group, aggregate) correctly implement the intended relational semantics.
    Section 3.4 toolset: implementation relies on pandas behavior; not further justified.

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Cite this review

Pith. "Pith review of Text to Query Plans for Question Answering on Large Tables." pith.science (2026). https://pith.science/paper/MDBFBLWB

@misc{pith2026250818758,
  author       = {Pith},
  title        = {Pith review of: Text to Query Plans for Question Answering on Large Tables},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MDBFBLWB}},
  note         = {Machine review of arXiv:2508.18758}
}
read the original abstract

Efficient querying and analysis of large tabular datasets remain significant challenges, especially for users without expertise in programming languages like SQL. Text-to-SQL approaches have shown promising performance on benchmark data; however, they inherit SQL's drawbacks, including inefficiency with large datasets and limited support for complex data analyses beyond basic querying. We propose a novel framework that transforms natural language queries into query plans. Our solution is implemented outside traditional databases, allowing us to support classical SQL commands while avoiding SQL's inherent limitations. Additionally, we enable complex analytical functions, such as principal component analysis and anomaly detection, providing greater flexibility and extensibility than traditional SQL capabilities. We leverage LLMs to iteratively interpret queries and construct operation sequences, addressing computational complexity by incrementally building solutions. By executing operations directly on the data, we overcome context length limitations without requiring the entire dataset to be processed by the model. We validate our framework through experiments on both standard databases and large scientific tables, demonstrating its effectiveness in handling extensive datasets and performing sophisticated data analyses.

Figures

Figures reproduced from arXiv: 2508.18758 by the authors.

Figure 1
Figure 1. The Example of Tree Structure Planning methods, we formulate the problem as finding the correct sequence of operations that, when applied to the datasets, yields the desired result according to the user’s query. Definition 1 (Problem Definition). Given a set of tabular data D = {𝑑1, 𝑑2, · · · 𝑑𝑚}, a user query 𝑞, and a set of operators on D denoted by O = {𝑜1, 𝑜2, · · · , 𝑜𝑛}, our objective is to generate a logical … view at source ↗
Figure 2
Figure 2. The Architecture of Tree-Driven Sequential Opera [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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