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arxiv: 2606.09578 · v1 · pith:K2LW6ON6new · submitted 2026-06-08 · 💻 cs.AI · cs.CL· cs.IR

TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMs

Momina Ahsan , Sarfraz Ahmad , Ming Shan Hee , Roy Ka-Wei Lee , Preslav Nakov This is my paper

Pith reviewed 2026-06-27 16:45 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.IR
keywords table understandingLLMsVLMsbenchmarkrepresentation effectsmultimodal evaluationquestion answeringstructure reconstruction
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The pith

Table representation substantially affects how LLMs and VLMs understand the same content.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents TABVERSE, a benchmark designed to hold table content fixed while varying its presentation across HTML, Markdown, LaTeX, and rendered images. This controlled setup isolates the impact of format and modality on three tasks: question answering, structural understanding, and structure reconstruction. Evaluation of multiple LLMs and VLMs shows that structured text formats generally outperform image renderings, though the performance gap varies with the specific task, model, and format chosen. HTML emerges as a particularly robust text format, while certain structural tasks remain difficult regardless of representation.

Core claim

By aligning identical table content across multiple structural formats and rendered images, TABVERSE reveals that representation choice substantially affects table understanding performance in LLMs and VLMs. Models generally perform better with structured text than with rendered images, but the size of this gap depends on the task, model, and format. HTML is often the most robust text format, while row-sensitive structural tasks and syntactically usable LaTeX reconstruction remain challenging.

What carries the argument

TABVERSE benchmark that aligns identical table content across structural text formats and images while tagging questions by category and difficulty.

If this is right

  • Representation must be treated as a controlled variable in any table reasoning evaluation.
  • Structured text inputs yield higher performance than image inputs for most models and tasks.
  • HTML tends to produce more consistent results than other text formats across the tested models.
  • Row-sensitive tasks and LaTeX reconstruction stay difficult even with optimal representations.
  • Benchmarks that let format vary with content cannot cleanly measure model capability.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Model developers may benefit from training on multiple representations of the same tables to increase format robustness.
  • Evaluation protocols for table tasks should specify and standardize the input format used.
  • Extending the benchmark to additional modalities or interactive table edits could reveal further representation sensitivities.
  • Practical systems might route table inputs to the format that matches the model's strongest performance.

Load-bearing premise

The benchmark can present the same table content in different formats and images without changing the underlying information that models must process.

What would settle it

An evaluation on new tables showing identical model accuracy across all text formats and image renderings would contradict the claim that representation choice substantially affects performance.

Figures

Figures reproduced from arXiv: 2606.09578 by Ming Shan Hee, Momina Ahsan, Preslav Nakov, Roy Ka-Wei Lee, Sarfraz Ahmad.

Figure 1
Figure 1. Figure 1: Overview of TABVERSE: From the balanced evaluation set, each table is represented in three structural formats (HTML, Markdown, LaTeX) with corresponding rendered images. These aligned multimodal pairs enable evaluation on QA, SUC, and SR tasks across VLMs and LLMs for cross-format and cross-modality analysis. Split Question–table pairs Unique tables Full tagged pool 6,097 4,434 Balanced set 700 629 [PITH_… view at source ↗
Figure 4
Figure 4. Figure 4: Prompts used for generating structured table [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 2
Figure 2. Figure 2: Prompt used to classify table-question pairs [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Prompts used for table-based QA and binary [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 6
Figure 6. Figure 6: TaskQA modality gap. ∆ accuracy (pp) = VLM-Text avg − VLM-Image avg, averaged over HTML/LaTeX/Markdown. Negative means images help more than text. Strict vs relaxed matching [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: TaskQA category averages. Accuracy by question category, averaged over models and formats, shown per pipeline (VLM-Image / VLM-Text / LLM￾Text). Easy vs Hard split [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Category-wise Task QA accuracy for the strongest model from each pipeline. Evaluation notes: TaskQA is scored with strict exact-match accuracy in the main results. We apply the same normalization and post-processing to all models. Strict EM is intentionally conservative: answers with extra explanatory text are counted as incorrect even when they contain the gold answer. For this reason, [PITH_FULL_IMAGE:f… view at source ↗
Figure 9
Figure 9. Figure 9: TaskQA Easy vs Hard. Easy and Hard exact-match accuracy per model and pipeline (VLM-Image / VLM-Text / LLM-Text), averaged over HTML/LaTeX/Markdown. Models marked with ∗ have shorter context win￾dows; when they fail to return an answer on large￾table cases, the output is counted as incorrect under the same scoring rule. This ensures consistent eval￾uation across architectures and context lengths. SUC Tasks… view at source ↗
Figure 10
Figure 10. Figure 10: Taxonomy of SUC tasks. Ten tasks are grouped into partitioning, size estimation, lookup, and retrieval [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: SUC task difficulty. We average exact￾match accuracy over all models, pipelines, and formats for each SUC subtask. Higher is better. D.2 Structural Understanding Capability: Additional Analyses This appendix provides additional SUC analyses that support Section 5.2 [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Pipeline comparison. We average exact-match accuracy per SUC subtask for VLM-Image, VLM-Text, and LLM-Text. Higher is better. Prompt explicitness and header handling: Ta￾ble 6 compares the explicit SUC prompt with an implicit prompt on selected VLM-Image subtasks. The explicit prompt states conventions such as excluding headers for first/last-cell tasks and us￾ing 0-indexed row/column coordinates for look… view at source ↗
Figure 13
Figure 13. Figure 13: Format sensitivity. We show mean exact-match accuracy by SUC subtask and format (HTML, LaTeX, Markdown) and the variation across formats. Higher is better [PITH_FULL_IMAGE:figures/full_fig_p021_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Pipeline for SR. A ground-truth table x is rendered into an image, the model predicts a structure x ′ , and evaluation compares x ′ with x. D.3 Structure Reconstruction: Additional Analyses [PITH_FULL_IMAGE:figures/full_fig_p021_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Format-pair difficulty (SR). We average GriTS-Topology and GriTS-Content over all models for each input→output format pair [PITH_FULL_IMAGE:figures/full_fig_p023_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Same-format vs cross-format SR. We compare per-model averages for same-format reconstruction against cross-format conversion. On lookup-style questions, many errors arise from selecting a plausible cell from the correct column but the wrong row, or from missing a filtering condi￾tion in Conditional Lookup. Similar failures occur in Comparison/Extremum questions when models compare values within the wrong … view at source ↗
read the original abstract

Large Language Models (LLMs) and Vision-Language Models (VLMs) are increasingly evaluated on table reasoning tasks, but the role of table representation remains under-explored. In practice, the same table content may appear in different structural formats, such as HTML, Markdown, and LaTeX, or as rendered images. However, existing evaluations often let content, format, layout, and modality vary together, making it difficult to isolate representation effects. We introduce TABVERSE, a controlled multimodal table benchmark that aligns the same table content across multiple structural formats and rendered images, with question category and difficulty tags. This design enables systematic evaluation of representation effects while holding table content fixed. We evaluate LLMs and VLMs across three tasks: Question Answering (QA), Structural Understanding Capability (SUC), and Structure Reconstruction (SR). Our results show that representation choice substantially affects table understanding. Models generally perform better with structured text than with rendered images, but the size of this gap depends on the task, model, and format. HTML is often the most robust text format, while row-sensitive structural tasks and syntactically usable LaTeX reconstruction remain challenging. These findings show that table representation is a key factor in reliable table evaluation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces TABVERSE, a controlled multimodal benchmark aligning identical table content across structural formats (HTML, Markdown, LaTeX) and rendered images, tagged with question categories and difficulties. It evaluates LLMs and VLMs on QA, Structural Understanding Capability (SUC), and Structure Reconstruction (SR) tasks, claiming that representation choice substantially affects performance, with structured text generally outperforming images (gap size varying by task/model/format), HTML often most robust, and row-sensitive or LaTeX reconstruction tasks remaining challenging.

Significance. If the central isolation of representation effects holds, the work provides a useful controlled benchmark for table reasoning evaluation and highlights representation as a key variable. The explicit design for holding content fixed while varying format is a methodological strength that could support more reliable comparisons than prior mixed-format evaluations.

major comments (2)
  1. [Abstract] Abstract: the claim that the benchmark 'aligns the same table content across multiple structural formats and rendered images, with question category and difficulty tags' while 'holding table content fixed' is load-bearing for all reported performance gaps, yet the manuscript provides no description of automated equivalence tests, round-trip fidelity checks, human audits, or canonical data-structure comparisons between structural and image versions.
  2. [Abstract] Abstract and evaluation description: no dataset sizes, number of tables/questions, error bars, or statistical significance tests are reported for the performance differences across representations; without these, the claim that 'representation choice substantially affects table understanding' and that 'the size of this gap depends on the task, model, and format' cannot be fully assessed for reliability.
minor comments (1)
  1. The abstract would be clearer if it specified the total number of tables, questions per task, and models evaluated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify the presentation of the TABVERSE benchmark's core methodological claims. We respond point by point to the major comments below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the benchmark 'aligns the same table content across multiple structural formats and rendered images, with question category and difficulty tags' while 'holding table content fixed' is load-bearing for all reported performance gaps, yet the manuscript provides no description of automated equivalence tests, round-trip fidelity checks, human audits, or canonical data-structure comparisons between structural and image versions.

    Authors: We agree that the abstract and construction section would benefit from an explicit account of equivalence verification to substantiate the claim that content is held fixed. The benchmark is generated from a single canonical table representation, but the manuscript does not currently detail the automated checks, round-trip fidelity procedures, human audits, or structural comparisons used. We will add a dedicated subsection describing these verification steps. revision: yes

  2. Referee: [Abstract] Abstract and evaluation description: no dataset sizes, number of tables/questions, error bars, or statistical significance tests are reported for the performance differences across representations; without these, the claim that 'representation choice substantially affects table understanding' and that 'the size of this gap depends on the task, model, and format' cannot be fully assessed for reliability.

    Authors: The experimental section reports the number of tables and questions along with performance metrics that include standard deviations. However, these quantities and the use of significance testing are not referenced in the abstract or high-level evaluation description. We will revise the abstract to include dataset sizes and add an explicit statement that error bars and statistical tests are provided in the results. This addresses the concern about assessing reliability. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark with direct evaluation

full rationale

The paper introduces TABVERSE as a controlled benchmark and reports empirical results on LLMs/VLMs across QA, SUC, and SR tasks. No equations, derivations, fitted parameters, or predictions appear in the provided text. The central claim (representation affects performance while holding content fixed) rests on benchmark construction and direct measurement rather than any self-definitional reduction, self-citation chain, or renaming of known results. The skeptic concern about format equivalence is a validity question, not a circularity reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical benchmark paper with no mathematical derivations. No free parameters, axioms, or invented entities are introduced.

pith-pipeline@v0.9.1-grok · 5765 in / 955 out tokens · 20329 ms · 2026-06-27T16:45:41.086951+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

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    Identify what the question asks for: • single value • list/set • number • yes/no

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    Identify which table cell(s) must be read or aggre- gated to produce the final answer

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    Verify that the final answer is directly read from or computed using ONLY table cells

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    If the table is not needed to produce the final answer →None of the above — Not table-required

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    cannot determine

    If the answer value is not present or cannot be com- puted from table cells → None of the above — Miss- ing answer attribute in table Otherwise, proceed to classification. QUESTION CATEGORIES (Choose EXACTLY ONE) • Simple LookupIdentify ONE row and read ONE cell. (No filtering beyond locating that row.) •Conditional LookupApply one or more conditions to s...