RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sector

Kun Zhou; Weijia Jia; Wenmian Yang; Yiquan Zhang; Zhensheng Wang

REVIEW 4 major objections 8 minor 1 cited by

RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sector

T0 review · 4 major / 8 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Intent and slot labels, added to queries as SLU annotations, improve open-domain long-table tabular question answering by guiding retrieval and answer generation.

desk verdict Useful new dataset, but SLUTQA's central claim is confounded by demonstration selection; worth sending to review with a request for fixes. read the letter →

arxiv 2412.10104 v2 pith:3EB3MSEN submitted 2024-12-13 cs.CL cs.AI

Zhensheng Wang , Wenmian Yang , Kun Zhou , Yiquan Zhang , Weijia Jia This is my paper · ORCID

classification cs.CLcs.AI

keywords RETQAtabularquestionansweringopen-domainQAspokenlanguageunderstandinglong-tablerealestatedatain-contextlearningBM25retrieval

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

This paper introduces RETQA, a large-scale open-domain Chinese tabular question-answering dataset for real estate, containing 4,932 tables and 20,762 question-answer pairs across property information, real estate company finance, and land auction domains. The authors argue that existing TQA datasets do not capture the real-world combination of long tables, open-domain retrieval, and multi-table queries, and they propose that adding spoken language understanding labels, namely intent and slot annotations, to each query helps large language models retrieve the right tables and produce accurate answers. To support this, the paper presents the SLUTQA framework, which uses SLU labels to generate table-caption-style query summaries for BM25 retrieval and to filter long tables or generate SQL before answering. Experiments show consistent gains over a vanilla pipeline across three LLMs in both markdown and SQL answer formats. If the claim holds, SLU labels are a cheap, effective annotation signal for open-domain long-table TQA.

What carries the argument

The load-bearing object is the SLU label pair attached to each query: an intent category drawn from 16 types and slot tags in Inside-Outside-Beginning format covering city, district, development name, company name, year, and month. The SR module puts these labels into few-shot prompts that rewrite the query into a table-caption-style summary, which is then matched against table captions with BM25; the SFA module uses the same labels to prune irrelevant rows and columns of retrieved long tables or to constrain SQL generation. Labels come either from a fine-tuned BERT model or from in-context learning with a handful of examples.

What would settle it

Run SLUTQA against a vanilla pipeline on a set of naturally collected, non-template real estate queries with human-annotated SLU labels, measuring retrieval F1 and answer exact match; if the SLU-based gains shrink to near the vanilla level, the claim that SLU labels drive the improvement would be refuted. A simpler check is whether an SR summary generated without SLU labels retrieves tables as well as the SLU-labeled version.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central empirical discovery is that intent and slot labels, treated as extra query annotations and injected into in-context learning prompts, materially improve both table retrieval and answer quality for open-domain long-table TQA. The paper reports that using SLU labels in the retrieval module raises table-retrieval F1 over the vanilla query-summary approach, and that using them in the filtering and SQL-generation module improves Table EM by about four percentage points in markdown format and ECR and pass@1 by more than 20 points in SQL format across Qwen2-7B, Qwen2-72B, and GLM4-9B. The paper frames RETQA as the first TQA dataset to include SLU labels, and SLUTQA as the first framework to combine SLU tasks with TQA tasks, with the claimed benefit coming without fine-tuning the LLM.

Load-bearing premise

The benchmark's queries, built from templates and LLM rewrites, have slot labels that align exactly with table captions and column headers, so real-world user queries that lack this clean alignment may not show the same gains.

Editorial extensions

If this is right

RETQA provides a benchmark where open-domain retrieval, long tables averaging 252.9 rows, multi-table queries, and multi-intent queries can be evaluated together in Chinese.
SLUTQA improves table-retrieval F1 and markdown and SQL answer accuracy over a vanilla pipeline on Qwen2-7B, Qwen2-72B, and GLM4-9B without fine-tuning the LLM.
Fine-tuning BERT for SLU prediction yields further gains, showing that better label quality translates into better TQA performance.
SLU-guided row and column filtering reduces the long-table bottleneck for LLMs with limited context windows.
Multi-table and multi-intent queries are explicitly labeled, enabling separate study of retrieval, filtering, and reasoning in open-domain table QA.

Reading between the lines

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

The reported gains may partly rely on RETQA's construction, where table captions were designed to match the variables in the query templates, making the SLU-to-caption mapping unusually clean; real-world deployment would need to test transfer to naturally collected queries.
SLU labels could be plugged into dense retrieval or embedding-based table search rather than only BM25, since the caption-style summary produced by the SR module is a compact query representation.
The same intent-plus-slot annotation scheme could transfer to other vertical domains with structured tables, such as healthcare or logistics, where long tables and open-domain retrieval also arise.
A testable extension is to measure how sensitive SLUTQA is to SLU prediction errors, since the framework's benefit depends on label quality.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The dataset is the real contribution here. RETQA gives the field 20,762 Chinese real estate QA pairs over long, multi-table, open-domain tables, with SLU labels and captions. That is a legitimate new resource, and the paper is honest about its construction and about the fact that no variance or significance tests were run (Appendix F). I think the dataset deserves a serious referee.

What I am less convinced by is the method claim. The main experiments compare a Vanilla baseline that uses five randomly selected ICL examples with SLUTQA, whose SR and SFA modules select demonstrations sharing the same intent. That changes two variables at once: presence of SLU labels and the demonstration-selection policy. The stress-test note is right. The only control that holds the selection policy fixed is Table 5's "Simplified" condition, and there Vanilla actually beats Simplified on Qwen2-7B, while on the other two models simplified but label-free prompting already captures a large part of the gain. No such matched control exists for SQL generation (Table 6) or for retrieval (Table 4). So the claim that SLU labels themselves drive the improvement is not established. The effect could largely be better demonstration selection.

There is also a circularity issue, as the reader flagged. The SLU labels are reverse-engineered from the 90 templates, and those templates determine the standardized table captions. The SR module generates a query summary from slots, which is essentially reconstructing the caption from components that came from the caption. That alignment likely inflates the retrieval gains. Real user queries with less clean label-caption correspondence may not benefit the same way.

The paper does some things well. The dataset statistics are thorough, the three answer formats are useful, and the authors are candid about the missing repeated runs. The BERT-based SLU results in Appendix D look strong, which is plausible. But the current experimental design does not isolate the value of SLU labels.

If I were the editor, I would send this to review but with a clear request: add an intent-matched, label-free baseline for all answer formats and retrieval, report variance and significance, and ideally include a small set of non-template queries to test the label-caption alignment issue. The dataset itself is probably worth publishing regardless of how the method ablations turn out. The paper is not a desk reject, but the central empirical claim needs rework before it can be accepted as stated.

Referee Report

4 major / 8 minor

Summary. The paper introduces RETQA, a Chinese open-domain tabular QA dataset for the real estate domain, with 4,932 tables and 20,762 QA pairs, and proposes SLUTQA, a framework that uses spoken language understanding (SLU) labels (intent and slot) to improve retrieval and answer generation with large language models via in-context learning. The dataset is constructed from public real estate data using 90 templates, with reverse-engineered SLU labels and LLM-rewritten queries, and answers are provided in Markdown, SQL, and natural language. Experiments on Qwen2-7b, Qwen2-72b, and GLM4-9b report that SLUTQA improves Markdown table exact match, SQL execution ratio/pass@1, and table retrieval F1 over a vanilla baseline. The paper includes ablations of the retrieval (SR) and answer (SFA) modules, but these ablations do not fully isolate the effect of SLU labels from the demonstration-selection policy, and the paper reports no variance, repeated runs, or significance tests.

Significance. If the empirical claims hold, RETQA would be a substantial new resource: it is the first large-scale open-domain Chinese TQA dataset for real estate, with long tables and multi-table queries, and it is the first TQA dataset to include SLU labels. The dataset construction is described transparently, the data sizes are large, and the code and data are promised for release. The idea of using SLU labels to guide table retrieval and filtering is intuitive and could be useful beyond this domain. However, the central contribution is compromised by the experimental design: the reported gains of SLUTQA over 'Vanilla' change two variables at once (SLU labels and demonstration selection), and the only condition that holds the selection policy fixed is limited to Markdown answers with gold labels. Without a control that isolates the SLU-label signal, the paper's headline claim that SLU labels 'significantly improve' performance is not established.

major comments (4)

[Experiments, 'Result and Analysis' (Tables 2-4, 6)] The main experiments compare a Vanilla baseline that uses 'five randomly selected examples' for ICL with SLUTQA, whose SR and SFA modules select demonstrations 'sharing the same intent' and inject intent/slot tokens into the prompt. This changes two factors at once: the presence of SLU labels and the demonstration-selection policy. Table 5's 'Simplified' condition is the only one that holds the selection policy fixed (intent-matched, no labels), but it is reported only for Markdown answers and uses gold SLU labels rather than predicted ones; no such control is provided for SQL generation (Table 6) or for query-summary-based retrieval (Table 4). A reader therefore cannot tell whether the improvements come from the SLU labels themselves or from better-matched demonstrations. Please add a no-label intent-matched baseline for SQL and retrieval experiments, and if possible a predicted-label condition for the Table 5 ablation.
[Appendix F and Tables 2-3] Appendix F states that no multiple runs, mean/variance statistics, or significance tests were performed. This is a load-bearing issue because several reported differences are small (e.g., Table EM improvements of 1.6-4.4 points absolute in Table 2, and retrieval F1 differences of 0.7-2.6 points in Table 4 for ICL vs Vanilla). Without variance estimates or significance tests, the word 'significantly' in the abstract and conclusions is unsupported, and the strength of the central claim cannot be evaluated. At minimum, report results over multiple runs with standard deviations and a paired test (e.g., bootstrap or approximate randomization) for the key comparisons.
[Dataset Construction, 'Intent and Slot Annotation' and 'QA Pair Generation'] The SLU labels are reverse-engineered from the 90 templates, and the table captions are themselves standardized from the same templates (e.g., 'Land Auction Information Table for {city district}'). The SR module generates a query summary from intent/slot values that were derived from the templates, and those slots align exactly with caption entities. This creates a closed loop: the labels are designed to match the retrieval targets, so high retrieval performance with SLU labels may reflect this artificial alignment rather than a general property of SLU-aware retrieval. The paper should either provide evidence that the template-derived labels transfer to naturally occurring user queries (e.g., a small human-written query set with manually annotated SLU labels) or moderate the claim that SLU labels improve open-domain TQA in general, as opposed to on this specific template-aligned benchmark.
[Experiments, 'Baselines' and Table 4] The description of the Vanilla baseline is inconsistent. In 'Baselines' the paper says the vanilla approach 'generated a query summary for BM25 retrieval and then produced the final answers using ICL with five randomly selected examples.' But in the SR ablation (Table 4), 'Vanilla' also uses a generated query summary for BM25 retrieval. This means the SR module is being compared against a baseline that already uses the same idea (summary-based retrieval), and the only difference is again the demonstration-selection policy and the presence of SLU labels. Please specify exactly what the Vanilla summary is, how it is generated, and whether the same example-selection policy is used; otherwise the retrieval comparison is not interpretable as an ablation of SLU labels.

minor comments (8)

[Method, 'SLU Module'] There is a typo: 'within the SLUQTA framework' should be 'SLUTQA framework'.
[Dataset Construction, 'Intent and Slot Annotation'] The slot type text contains an odd typo: '“month”,’' has an extra quote mark. Please fix.
[Method, 'SFA Module'] In the SQL-format description, 'the SLA module for SQL formats' should be 'the SFA module'.
[Abstract and Code availability] The abstract says the code is at github.com/jensen-w/RETQA, but the paper's code line says github.com/jensenw1/RETQA; please make these consistent.
[Table 1] The legend for Table 1 only explains '–', but the table uses '%' and '!' symbols; please define all symbols in the caption or legend.
[Appendix D and Method, 'SLU Module'] The method section says '22 examples' for few-shot SLU prediction, while Appendix D says 'a sufficient number of samples'; please reconcile these numbers or specify the exact setting.
[Method, 'SR Module'] For multi-intent queries, the selection of 'five examples from the training set that share the same intent' is under-specified: please clarify whether the example must match all predicted intents or any one of them, and how ties are broken.
[Related Works, 'TQA Datasets'] The dataset comparison in Table 1 would be more informative if it included a Chinese TQA dataset or another open-domain long-table benchmark; currently the comparison is mostly against English datasets, which makes the 'first Chinese' claim harder to evaluate.

Circularity Check

1 steps flagged · score 6.0 of 10

RETQA's slot-to-caption alignment makes SLU-label retrieval gains partly self-definitional; main Vanilla/SLUTQA comparison also confounds SLU labels with intent-matched ICL selection.

self definitional [Dataset Construction, 'Intent and Slot Annotation'; Method, 'SR Module'; Table 4]
""For slot labels, we categorize the entities into six types: "city", "district", "development name", "company name", "year", and "month", based on the entity types in the query templates and corresponding table headers." ... "we propose the SR module, which utilizes ICL with SLU labels to enable the large LLMs to generate a query summary in the form of a table caption.""

Slot categories are defined from the same query templates and table headers that determine the standardized captions; on RETQA a caption is a fixed pattern over exactly those slot values (e.g., city, district, year, month). The SR module's query summary is an LLM re-emission of the caption pattern from the slots, so retrieving the gold caption via BM25 is close to inverting the dataset's caption template. The retrieval gain in Table 4 therefore reflects the constructional alignment between slots and captions rather than a general property of SLU labels for arbitrary open-domain tables.

full rationale

The strongest circular element is the construction loop shared by the dataset and the SR module: the six slot types are extracted from the query templates and table headers, and the same headers/templates determine the standardized captions that serve as retrieval targets. An SR prompt that is asked to turn slots into a 'query summary in the form of a table caption' is therefore being asked to re-generate the caption template from its own ingredients, so the Table 4 retrieval improvements are partly a self-fulfilling benchmark property. This does not collapse the whole paper: the SFA markdown ablation (Table 5) compares Simplified vs SFA under gold tables/labels and holds demonstration selection closer to fixed, giving an independent test of SLU labels in answer generation. However, the headline Vanilla-vs-SLUTQA comparisons in Tables 2, 3, 4, and 6 change two variables at once (SLU labels and ICL demonstration selection: Vanilla uses five randomly selected examples, while SR/SFA select examples sharing the predicted intent), so the size of the claimed 'significant improvement' is not attributable to SLU labels alone; only Table 5 provides an appropriate control, and only for markdown answers. Appendix F candidly states that no repeated runs or significance tests were completed, consistent with the absence of variance reporting. The self-citations (Cheng/Yang/Jia 2023; Zhu et al. 2024) are used for background on SLU tasks and are not load-bearing. Overall, partial circularity via the caption/slot construction loop, plus an important confound, yields a score of 6.

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

The central empirical claim rests on design choices including template-derived SLU labels, standardized captions, and hand-picked example counts, plus assumptions that the constructed dataset reflects open-domain TQA difficulty. No new physical or mathematical entities are introduced.

free parameters (5)

SLU few-shot example count = 22
Hand-chosen number of training examples used to prompt LLMs for SLU prediction in the ICL scenario; affects SLUTQA (ICL) results.
SR module example count = 5
Hand-chosen number of same-intent examples used to generate query summaries in the SR module.
SFA module example count = 2-5
Hand-chosen number of examples, varied by total length, used in the SFA module for markdown and SQL generation.
Query template count = 90
Hand-designed set of templates that define the dataset's question distribution and the reverse-engineered SLU labels.
Intent category count = 16
Hand-defined taxonomy of query intents used for SLU labels and for selecting same-intent in-context examples.

assumptions (4)

domain assumption LLM-rewritten queries preserve the slot and intent labels assigned to the original template queries.
SLU labels are annotated before rewriting and are not re-annotated afterward; manual review of 291 pairs is the only check. Section 'LLM-based Query Rewriting and Quality Control'.
domain assumption Standardized table captions are sufficient retrieval keys, and the query summary format matches them.
The SR module relies on BM25 matching between the generated summary and table captions; this assumes caption format and summary format are comparable. Section 'SR Module'.
domain assumption Executable SQL generated from templates is a correct proxy for the answer.
QA pairs are filtered on SQL executability and non-empty results, and answers are derived from SQL execution. Section 'QA Pair Generation'.
domain assumption DeepSeek-V1's naturalness scores are a valid measure of query quality.
Rewrite quality is evaluated by an LLM judge with no reported human agreement. Section 'LLM-based Query Rewriting and Quality Control'.

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Cite this review

Pith. "Pith review of RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sector." pith.science (2026). https://pith.science/paper/3EB3MSEN

@misc{pith2026241210104,
  author       = {Pith},
  title        = {Pith review of: RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sector},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3EB3MSEN}},
  note         = {Machine review of arXiv:2412.10104}
}

read the original abstract

The real estate market relies heavily on structured data, such as property details, market trends, and price fluctuations. However, the lack of specialized Tabular Question Answering datasets in this domain limits the development of automated question-answering systems. To fill this gap, we introduce RETQA, the first large-scale open-domain Chinese Tabular Question Answering dataset for Real Estate. RETQA comprises 4,932 tables and 20,762 question-answer pairs across 16 sub-fields within three major domains: property information, real estate company finance information and land auction information. Compared with existing tabular question answering datasets, RETQA poses greater challenges due to three key factors: long-table structures, open-domain retrieval, and multi-domain queries. To tackle these challenges, we propose the SLUTQA framework, which integrates large language models with spoken language understanding tasks to enhance retrieval and answering accuracy. Extensive experiments demonstrate that SLUTQA significantly improves the performance of large language models on RETQA by in-context learning. RETQA and SLUTQA provide essential resources for advancing tabular question answering research in the real estate domain, addressing critical challenges in open-domain and long-table question-answering. The dataset and code are publicly available at \url{https://github.com/jensen-w/RETQA}.

Figures

Figures reproduced from arXiv: 2412.10104 by the authors.

**Figure 2.** General framework of SLUTQA. where y I ∈ R di , y N ∈ R dn , and y S = y S 1 , ..., y S n ∈ R ds represent the predicted results of intents, number of intent, and slots, respectively, WI ∈ R di×dmodel , WN ∈ R dn×dmodel , and WS ∈ R ds×dmodel are fully connected matrices, b I ∈ R di ,b N ∈ R dn and b S ∈ R ds are bias vectors. In this paper, di = 16, dn = 2, and ds = 13 (6 kinds of slots with B-tags, I-tags, and t… view at source ↗

**Figure 3.** Showcase of Template Filling. Dataset Example query:在2019年1月，北京市石景山区成交的地块里面，哪一个地块的容积率是最低的？ intent:["容积率查询","地块成交时间查询"] slots: [O,B-year,I-year,I-year,I-year,I-year,B-month,Imonth,O,B-city,I-city,I-city,B-district,I-district,Idistrict,I-district,O,O,O,O,O,O,O,O,O,O,O,O,O,O,O, O,O,O,O,O,O,O] table_caption_label:北京市石景山区土地成交信息表 SQL:SELECT "地块名称" FROM "北京市石景山区土地成交信息表" WHERE "时间(月份)" = '2019 年01月' ORDER BY "容积率" DESC… view at source ↗

Figures from the paper (2 more)

**Figure 5.** Figure 5: Visualizing Distribution of Rewrite Scores [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 4.** Figure 4: Showcase of a Example in the Dataset. 0% 20% 40% 60% 80% 100% Rewrite Origin Score 0 1 2 3 4 5 [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

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2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Badaro, G.; Saeed, M.; and Papotti, P. 2023. Transformers for Tabular Data Representation: A Survey of Models and Applications. Transactions of the Association for Computational Linguistics, 11: 227--249

work page 2023

[4] [4]

Chen, W. 2023. Large Language Models are few (1)-shot Table Reasoners. In Findings of the Association for Computational Linguistics: EACL 2023, 1120--1130

work page 2023

[5] [5]

Cheng, L.; Yang, W.; and Jia, W. 2023. A scope sensitive and result attentive model for multi-intent spoken language understanding. In Proceedings of the AAAI Conference on Artificial Intelligence, 12691--12699. AAAI Press

work page 2023

[6] [6]

DeepSeek-AI. 2024. DeepSeek LLM: Scaling Open-Source Language Models with Longtermism. arXiv preprint arXiv:2401.02954

arXiv 2024

[7] [7]

Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171--4186

2019

[8] [8]

A.; Zhang, J.; Hu, Z.; Qi, Y

Fang, X.; Xu, W.; Tan, F. A.; Zhang, J.; Hu, Z.; Qi, Y. J.; Nickleach, S.; Socolinsky, D.; Sengamedu, S.; Faloutsos, C.; et al. 2024. Large language models (LLMs) on tabular data: Prediction, generation, and understanding-a survey. arXiv preprint arXiv:2402.17944

arXiv 2024