Pith. sign in

REVIEW 2 major objections 4 minor 31 references

Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering

T0 review · 2 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read PieTa selects a union subtable through an iterative divide-and-conquer on table windows, improving table QA exact match over prior subtable selectors.

desk verdict A solid, incremental subtable-selection method whose reported QA gains hold up, but whose 'multi-resolution' framing overstates a monotone pruning loop; referee it, but ask for WikiTQ cell-level recall and code. read the letter →

arxiv 2412.07629 v4 pith:WAYHKWMO submitted 2024-12-10 cs.CL cs.AI

Wonjin Lee , Kyumin Kim , Sungjae Lee , Jihun Lee , Kwang In Kim This is my paper · ORCID
classification cs.CLcs.AI
keywords tablequestionansweringsubtableselectiondivideandconquerlanguagemodelscoordinaterepresentationunionwindowsexactmatch
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 tries to establish that a practical subtable selector can be built from a divide-and-conquer strategy: slice a large table into overlapping windows, ask a fine-tuned language model to mark the relevant cells inside each window, merge the marked cells into a union subtable, and repeat until the subtable stops changing. The central claim is that this union subtable, although looser than the ideal 'gold' subtable that intersects all conditions, is accurate enough that feeding it to an existing reader improves exact-match answers on WikiTableQuestions and WikiSQL over both holistic readers and prior subtable selection methods. A sympathetic reader would care because the approach sidesteps the long-context limits of transformers and only requires the selector to reason about small local windows. The paper's headline evidence is a WikiTQ exact match of 63.65 with a GPT-3.5 reader, versus 59.21 for a leading SQL-decomposition baseline and 59.13 for a dense-retrieval baseline.

What carries the argument

The load-bearing mechanism is the coordinate-based union subwindow selection loop: each window is a w×w slice of the current table, the selector predicts a same-shaped grid of coordinate tokens marking relevant cells, and the cells marked anywhere are merged into a new table. What this does is convert a global table-reasoning problem into a series of local cell-classification problems, letting the language model work within a short context while the iteration captures long-range dependencies. A second piece of machinery is the training target construction: subwindows are labeled by selecting condition columns and answer columns within each window and then keeping the rows that satisfy all conditions present in that window, with balancing and value-based augmentation. The paper also introduces the coordinate representation as an alternative to index-based and full-table representations, which they show degrades less on cell-embedded conditions and avoids cascading generation errors.

What would settle it

Take a WikiTQ test question whose gold answer requires two condition columns that never appear together in any 4×4 window containing the answer cells, and check whether the final union subtable still contains the gold answer row; if a nontrivial fraction of such cross-window questions lose their answer cells, the local-window sufficiency assumption is violated. A direct numerical version would compare the oracle union of per-window gold selections to the gold subtable and show the gap in exact match on the test set.

Watch

Extended reading notes

Core claim

Given a question and a table, PieTa constructs a subtable by iteratively dividing the current table into w×w windows with a sliding stride of 1, using a fine-tuned Llama3.1 selector to emit coordinate tokens (such as <2,3> or <empty,empty>) that mark which cells in each window answer the question, and then taking the set union of all selected cells as the next table. This loop runs until a fixed point is reached, with the window size w fixed at 4. The key discovery is that the resulting union subtable, which is about 13.9% of the original table's cell count, performs nearly as well as the theoretical gold subtable in downstream QA while being far easier to generate, and it outperforms existing subtable selectors on exact match across multiple readers. The coordinate representation is what makes this possible: it preserves the original window structure, so a condition matching a cell value rather than a column header can still be selected, and it avoids the error compounding of autoregressive table generation.

Load-bearing premise

The selector trained on windows from SQUALL and WikiSQL tables must generalize to arbitrary test windows, and for every window the question's answer can be determined from the condition and answer columns present within that window alone, so the union of per-window selections covers the needed evidence.

Editorial extensions

If this is right

  • On WikiTQ, PieTa with a GPT-3.5 reader reaches 63.65 exact match, outperforming TabSQLify (59.21) and ITR (59.13) with the same reader class.
  • On WikiSQL, PieTa with TaPEx reaches 91.83 exact match versus 89.31 for the holistic TaPEx baseline, and also exceeds the holistic OmniTab baseline.
  • Union subtables average 13.91% of the original table's cells, so downstream readers work with far shorter contexts while losing little accuracy relative to gold subtables.
  • Subtable selection recall stays above 99% across WikiSQL test conditions while precision improves by 47.43 percentage points over ITR, indicating the union strategy is robust across answer-row and condition-column counts.
  • Varying table size from under 50 to over 400 cells, PieTa keeps EM higher than ITR, Dater, TabSQLify, and holistic readers.

Reading between the lines

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

  • The same window-union recipe could transfer to long-document QA or multi-hop retrieval, where local evidence must be combined across distant spans.
  • The paper's result suggests a general design principle: relax a global selection into a union of local selections; precision suffers slightly but recall stays high, which may be the right trade for downstream models.
  • A testable extension is to replace the fixed window with content-aware windowing (e.g., grouping by column semantics) to reduce the number of iterations and further shrink the union subtable.
  • Because the selector is reader-agnostic, jointly fine-tuning the selector and reader could close the remaining gap to gold-subtable performance, as the paper notes in its limitations section.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 4 minor

Summary. PieTa is a subtable-selection framework for table question answering. Given a question and a table, the method iteratively slides w×w windows over the current table, prompts a fine-tuned Llama-3.1 selector to mark relevant cells in each window (using a coordinate representation), and unions the selected cells into a new, smaller table; iteration stops when the table no longer changes. The resulting subtable is then fed to an existing reader. Training targets are derived from SQL annotations (SQUALL for WikiTQ, WikiSQL for WikiSQL) by selecting condition and answer columns within each window and retaining rows that satisfy the conditions present in that window. The paper reports subtable-selection precision/recall on WikiSQL and end-to-end exact-match accuracy on WikiTQ and WikiSQL with multiple readers, together with ablations of learning mode, output representation, and window size. The central claim is that this simple iterative union procedure outperforms prior subtable-selection and holistic baselines, with headline results of 63.65 EM for PieTa+GPT-3.5 on WikiTQ versus 59.21 for TabSQLify and 91.83 for PieTa+TaPEx on WikiSQL versus 89.31 for the holistic baseline.

Significance. If the claimed results hold, PieTa is a useful and unusually simple contribution to subtable-based table QA. Its strengths are the explicit algorithms, the coordinate-output representation that addresses a real weakness of index- and table-based selection, the comparison across multiple readers and baselines, and a meaningful ablation study. The reported WikiSQL selection recall (99.65% first-pass, 99.12% final in Table 2) suggests the selector is effective on that dataset, and the WikiTQ QA gains are nontrivial. The main weakness is that the algorithm's decisive structural property—its monotone pruning, whereby any cell omitted in the first divide/conquer pass is permanently lost—is not directly evaluated on WikiTQ, the harder dataset. The paper is not circular: the QA readers are fixed external models and the selection targets are derived from SQL annotations. However, the evidence gap around first-pass recall on WikiTQ is load-bearing for the claim that the method captures multi-row and multi-column dependencies.

major comments (2)
  1. [Section 3.1, Algorithm 1 (lines 6-16)] The iterative loop is monotone decreasing: each V_i^t is a subwindow of its input window W_i, the windows cover T^t, and therefore T^{t+1} = union_i V_i^t is always a subset of T^t. As a result, any cell not selected in the first divide/conquer pass is gone forever, and the final subtable is a subset of the first-pass union. This makes first-pass recall the sole source of recall for the entire pipeline. The paper's only cell-level selection recall evidence is Table 2 on WikiSQL (first-pass recall 99.65, final recall 99.12); no analogous first-pass or final recall is reported for WikiTQ, which contains the harder questions (superlatives, comparisons, multi-row dependencies) and larger tables. The text's claim in Section 3.1 and the Conclusion that the iterative merging 'captures dependencies across multiple rows and columns' is not supported by the algorithm as written: no operation combines evidence across windows to select a cell that was not already selected locally. The authors should measure first-pass and final subtable recall on WikiTQ (for example, on a manually annotated sample or through a clearly described proxy) and provide failure analysis for cases where the required answer cell is never selected.
  2. [Section 3.2, data generation and target construction] The training targets are constructed from SQL annotations by selecting condition and answer columns within each w×w window and then keeping only rows that satisfy all conditions present in that window. This assumes that for every relevant window, the condition cells and answer cells are jointly visible within the window, or that a window lacking a condition imposes no row restriction. The paper does not quantify how often this local-evidence assumption fails, particularly on WikiTQ, where a question may require comparing or ordering cells across distant rows or columns (e.g., the 'next aircraft after Cessna 404 Titan' example in Section 1). Because of the monotone pruning property of Algorithm 1, a single failure of this assumption in the first pass cannot be repaired by later iterations. The paper should estimate the frequency of such local-evidence failures on the test sets, or otherwise demonstrate that the selector can propagate information across windows.
minor comments (4)
  1. [Section 3.4 and Figure 4] The example question is phrased as 'List each of engines with diesel fuel produced from 1988-2001' in the text but as 'produced from 1998-2001' in the Figure 4 caption and in the table values; the year should be made consistent.
  2. [Appendix A.5] The enumeration of possible target window sizes as {[1,1], ..., [w,w]} omits the zero-row cases [0,1]...[0,w] that are later discussed and appear in the histogram; the text should reconcile the enumeration with the actual sample space.
  3. [Appendix A.2, Algorithm 2] The loop bounds in Algorithm 2 are confusing: the 'for i = 0 to R by 1' loop with an assignment 'i = R - w' inside the body suggests clamping, but as written it may re-process or skip windows; rewriting the loops as 'for i = 0 to R-w' and 'for j = 0 to C-w' would make the sliding-window behavior unambiguous.
  4. [Section 4.1, TabSQLify comparison] The text says TabSQLify was evaluated with both Llama3.1 and GPT-3.5 and the better model was selected, but the Llama3.1 result is not reported; reporting it would make the comparison more transparent.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the QA evaluation uses fixed external readers (TaPEx, OmniTab, GPT-3.5), and the subtable-selection metrics are supervised held-out evaluations, not fitted inputs renamed as predictions.

full rationale

The paper's central QA claim is not circular. PieTa's output subtable is fed to fixed external readers (TaPEx, OmniTab, GPT-3.5, DP&Agent) that are not trained by the authors, so the EM scores measure an external function of the selected subtable and are not a quantity the selector was fit to reproduce. The subtable-selection results in Table 2 are measured against SQL-derived gold labels, and the selector is trained on targets constructed by the same SQL-derived rule; this is a standard supervised held-out evaluation, not a fitted parameter renamed as a prediction. The paper explicitly states that experiments use clearly separated training, validation, and test sets, so the held-out labels are not the training inputs. The iterative algorithm's monotone-shrinking property (T^{t+1} is a union of subwindows of T^t, hence a subset of T^t) is a real structural limitation for recall, but it is a correctness and evaluation concern, not circularity: Table 2 quantifies the recall trade-off, and the QA claim does not reduce to the iteration claim. There are no load-bearing self-citations in the reference list, and the coordinate representation, window size, and union operations are presented as design choices rather than results imported from prior work by the same authors. Therefore no derivation step in the paper is equivalent to its input by construction.

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

The central claim depends on a small set of engineering hyperparameters (window size, augmentation rates, training schedule) rather than on invented scientific entities. The balancing scheme and the SQL-derived target construction are the most consequential unverified assumptions, because they shape what the selector learns and how the subtable selection metric is defined.

free parameters (5)
  • window size w = 4
    The paper fixes w = 4 for all experiments and reports that 3x3 performs the same, 5x5 slightly worse, and 4xn notably worse, so the choice is a hand-tuned hyperparameter rather than a derived quantity.
  • data augmentation percentage = 8% (WikiTQ), 21% (WikiSQL)
    The augmentation rate of labeling same-valued cells as targets is a hand-chosen data construction choice that affects the training distribution of the selector.
  • number of training epochs = 1
    The selector is trained for a single epoch, which is a reported experimental choice and not justified by a validation sweep in the paper.
  • learning rate = 1e-5
    The AdamW learning rate is a standard fine-tuning choice reported in the appendix; it is not derived from the method.
  • class balancing of target window sizes = uniform over n and m
    The authors explicitly adjust the target window size distribution to mitigate imbalance, introducing a distributional assumption about which target sizes should be equally likely in training.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering." pith.science (2026). https://pith.science/paper/WAYHKWMO

@misc{pith2026241207629,
  author       = {Pith},
  title        = {Pith review of: Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WAYHKWMO}},
  note         = {Machine review of arXiv:2412.07629}
}
read the original abstract

Applying language models (LMs) to tables is challenging due to the inherent structural differences between two-dimensional tables and one-dimensional text for which the LMs were originally designed. Furthermore, when applying linearized tables to LMs, the maximum token lengths often imposed in self-attention calculations make it difficult to comprehensively understand the context spread across large tables. To address these challenges, we present PieTa (Piece of Table), a new framework for subtable-based question answering (QA). PieTa operates through an iterative process of dividing tables into smaller windows, using LMs to select relevant cells within each window, and merging these cells into a subtable. This multi-resolution approach captures dependencies across multiple rows and columns while avoiding the limitations caused by long context inputs. Instantiated as a simple iterative subtable union algorithm, PieTa demonstrates improved performance over previous subtable-based QA approaches.

Figures

Figures reproduced from arXiv: 2412.07629 by the authors.

Figure 1
Figure 1. An overview of the proposed PieTa (Piece of Table) framework. Starting with an input table and a question, our algorithm synthesizes a subtable by iter￾atively dividing the table into smaller windows, using language models to select relevant cells within these windows (forming intermediate subtables), and merging these until the final subtable is constructed. Code will be made publicly available upon acceptance. mea… view at source ↗
Figure 2
Figure 2. Examples of generated training data with an window size of [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Examples of subtable representations (w = 3). The information matching the condition Total is lo￾cated in a cell rather than the column header. Both the index and table representations fail to select the cell Total because the column Description Losses is not selected. The proposed coordinate representation over￾comes this limitation by preserving the input window structure and relevant cell contents. question seeks… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Examples of (sub)tables and the corresponding QA results. The original table has two condition columns [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Table QA performance (EM; %) across vary [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

31 extracted references · 13 canonical work pages

  1. [1]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

  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]

    Gregor Bachmann and Vaishnavh Nagarajan. 2024. https://proceedings.mlr.press/v235/bachmann24a.html The pitfalls of next-token prediction . In ICML

    work page 2024

  4. [4]

    Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. 2015. https://proceedings.neurips.cc/paper_files/paper/2015/file/e995f98d56967d946471af29d7bf99f1-Paper.pdf Scheduled sampling for sequence prediction with recurrent neural networks . In NeurIPS

    work page 2015

  5. [5]

    Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian...

    arXiv 2021

  6. [6]

    Smith, and Tao Yu

    Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu. 2023. https://openreview.net/forum?id=lH1PV42cbF Binding language models in symbolic languages . In ICLR

    work page 2023

  7. [7]

    Jonathan Herzig, Pawel Krzysztof Nowak, Thomas M \"u ller, Francesco Piccinno, and Julian Eisenschlos. 2020. https://doi.org/10.18653/v1/2020.acl-main.398 T a P as: Weakly supervised table parsing via pre-training . In ACL

    doi:10.18653/v1/2020.acl-main.398 2020

  8. [8]

    Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Xin Zhao, and Ji-Rong Wen. 2023. https://aclanthology.org/2023.emnlp-main.574/ S truct GPT : A general framework for large language model to reason over structured data . In EMNLP

    work page 2023

Show all 31 references
  1. [9]

    Zhengbao Jiang, Yi Mao, Pengcheng He, Graham Neubig, and Weizhu Chen. 2022. https://doi.org/10.18653/v1/2022.naacl-main.68 O mni T ab: Pretraining with natural and synthetic data for few-shot table-based question answering . In NAACL

    2022 doi

  2. [10]

    Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. https://doi.org/10.18653/v1/2020.emnlp-main.550 Dense passage retrieval for open-domain question answering . In EMNLP

    2020 doi

  3. [11]

    Alex Lamb, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron Courville, and Yoshua Bengio. 2016. https://proceedings.neurips.cc/paper_files/paper/2016/file/16026d60ff9b54410b3435b403afd226-Paper.pdf Professor forcing: A new algorithm for training recurrent networks . In NeurIPS

    2016

  4. [12]

    Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2020. https://doi.org/10.18653/v1/2020.acl-main.703 BART : Denoising sequence-to-sequence pre-training for natural language generation, translation, a...

    2020 doi

  5. [13]

    Peng Li, Yeye He, Dror Yashar, Weiwei Cui, Song Ge, Haidong Zhang, Danielle Rifinski Fainman, Dongmei Zhang, and Surajit Chaudhuri. 2024. https://doi.org/10.1145/3654979 Table-GPT : Table fine-tuned GPT for diverse table tasks . Proc. ACM Management of Data (PACMMOD)

    2024 doi

  6. [14]

    Weizhe Lin, Rexhina Blloshmi, Bill Byrne, Adria de Gispert, and Gonzalo Iglesias. 2023. https://doi.org/10.18653/v1/2023.acl-long.551 An inner table retriever for robust table question answering . In ACL

    2023 doi

  7. [15]

    Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang

    Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024 a . https://doi.org/10.1162/tacl_a_00638 Lost in the middle: How language models use long contexts . Trans. ACL

    2024 doi

  8. [16]

    Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, and Jian-Guang Lou. 2022. https://openreview.net/forum?id=O50443AsCP TaPEx : Table pre-training via learning a neural SQL executor . In ICLR

    2022

  9. [17]

    Tianyang Liu, Fei Wang, and Muhao Chen. 2024 b . https://doi.org/10.18653/v1/2024.naacl-long.26 Rethinking tabular data understanding with large language models . In NAACL

    2024 doi

  10. [18]

    Md Mahadi Hasan Nahid and Davood Rafiei. 2024. https://aclanthology.org/2024.naacl-long.320 Tab SQL ify: Enhancing reasoning capabilities of LLM s through table decomposition . In NAACL

    2024

  11. [19]

    Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Nick Schoelkopf, Riley Kong, Xiangru Tang, Murori Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, and Dragomir Radev. 2022. https://doi...

    2022 doi

  12. [20]

    Wang, and Xi Victoria Lin

    Ansong Ni, Srini Iyer, Dragomir Radev, Ves Stoyanov, Wen-tau Yih, Sida I. Wang, and Xi Victoria Lin. 2023. https://proceedings.mlr.press/v202/ni23b.html LEVER : Learning to verify language-to-code generation with execution . In ICML

    2023

  13. [21]

    Panupong Pasupat and Percy Liang. 2015. https://doi.org/10.3115/v1/P15-1142 Compositional semantic parsing on semi-structured tables . In ACL-IJCNLP

    2015 doi

  14. [22]

    Sohan Patnaik, Heril Changwal, Milan Aggarwal, Sumit Bhatia, Yaman Kumar, and Balaji Krishnamurthy. 2024. https://openreview.net/forum?id=SQrHpTllXa CABINET : Content relevance-based noise reduction for table question answering . In ICLR

    2024

  15. [23]

    Stephane Ross and Drew Bagnell. 2010. https://proceedings.mlr.press/v9/ross10a.html Efficient reductions for imitation learning . In ICML

    2010

  16. [24]

    Tianze Shi, Chen Zhao, Jordan Boyd-Graber, Hal Daum \'e III, and Lillian Lee. 2020. https://doi.org/10.18653/v1/2020.findings-emnlp.167 On the potential of lexico-logical alignments for semantic parsing to SQL queries . In Findings of EMNLP

    2020 doi

  17. [25]

    Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, and Tomas Pfister. 2024. https://openreview.net/forum?id=4L0xnS4GQM Chain-of-Table : Evolving tables in the reasoning chai...

    2024

  18. [26]

    Yuanzhen Xie, Xinzhou Jin, Tao Xie, Mingxiong Lin, Liang Chen, Chenyun Yu, Lei Cheng, Chengxiang Zhuo, Bo Hu, and Zang Li. 2024. https://aclanthology.org/2024.findings-acl.641.pdf Decomposition for enhancing attention: Improving LLM -based text-to- SQL through workflow paradig...

    2024

  19. [27]

    Jingfeng Yang, Aditya Gupta, Shyam Upadhyay, Luheng He, Rahul Goel, and Shachi Paul. 2022. https://doi.org/10.18653/v1/2022.acl-long.40 T able F ormer: Robust transformer modeling for table-text encoding . In ACL

    2022 doi

  20. [28]

    Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, and Yongbin Li. 2023. https://doi.org/10.1145/3539618.3591708 Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning . In SIGIR

    2023

  21. [29]

    Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel. 2020. https://doi.org/10.18653/v1/2020.acl-main.745 T a BERT : Pretraining for joint understanding of textual and tabular data . In ACL

    2020 doi

  22. [30]

    Yunjia Zhang, Jordan Henkel, Avrilia Floratou, Joyce Cahoon, Shaleen Deep, and Jignesh M. Patel. 2024. https://doi.org/10.14778/3659437.3659452 ReAcTable : Enhancing ReAct for table question answering . Proc. VLDB Endow

    2024

  23. [31]

    Victor Zhong, Caiming Xiong, and Richard Socher. 2017. https://arxiv.org/abs/1709.00103 Seq2SQL : Generating structured queries from natural language using reinforcement learning . arXiv:1709.00103

    2017 arXiv

Pith tools

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