TABLET: A Large-Scale Dataset for Robust Visual Table Understanding
Authors: Iñigo Alonso
Imanol Miranda
Eneko Agirre
Mirella Lapata
Venue: ICLR 2026 Poster (2026)
Keywords: Visual Table Understanding
Table Understanding
Datasets
Visually Represented Language
Multimodal Table Understanding
Primary area: datasets and benchmarks
TL;DR: We introduce TABLET, a large-scale VTU dataset with 4 million examples across 21 tasks, grounded in 2 million unique tables where 88% preserve original visualizations.

Abstract: While table understanding increasingly relies on pixel-only settings, current benchmarks predominantly use synthetic renderings that lack the complexity and visual diversity of real-world tables. Additionally, existing visual table understanding (VTU) datasets offer fixed examples with single visualizations and pre-defined instructions, providing no access to underlying serialized data for reformulation. We introduce TABLET, a large-scale VTU dataset with 4 million examples across 21 tasks, grounded in 2 million unique tables where 88% preserve original visualizations. To evaluate whether models are able to jointly reason over tabular and visual content, we also introduce VisualTableQA, a benchmark requiring both visual perception and table understanding. Fine-tuning vision-language models like Qwen2.5-VL-7B and Gemma 3-4B on TABLET improves performance on seen and unseen VTU tasks while increasing robustness on real-world table visualizations. By preserving original visualizations and maintaining example traceability in a unified large-scale collection, TABLET establishes a foundation for robust training and extensible evaluation of future VTU models.

Published as a conference paper at ICLR 2026
TABLET: A LARGE-SCALE DATASET FOR ROBUST VISUAL TABLE UNDERSTANDING
Iñigo Alonso', Imanol Miranda", Eneko Agirre?, Mirella Lapata'
'School of Informatics, University of Edinburgh {ialonso, miap)@ed. ac uk
HiTT Center Ixa, University of the Basque Country UPV/EHU [imanol. miranda, eneko. agirrreeehreee.ee....
ABSTRACT
While table understanding increasingly relies on pixel-only settings, current benchmarks predominantly use synthetic renderings that lack the complexity and visual diversity of real-world tables. Additionally, existing visual table under- standing (VTU) datasets offer fixed examples with single visualizations and pre- defined instructions, providing access to underlying serialized data for reformu- lation. We introduce TABLET, a large-scale VTU dataset with 4 million cxamples across 21 tasks, grounded in 2 million unique tables where 88% preserve original visualizations. To evaluate whether models are able to jointly reason over tabu- lar and visual content, also introduce VisualTableQA, benchmark requiring both visual perception and table understanding. Fine-tuning vision-language mod- els like Qwen2.5-VL-7B and Gemma 3-4B on TABLET improves performance on seen and unseen VTU tasks while increasing robustness on real-world table visu- alizations. By preserving original visualizations and maintaining example trace- ability in a unified large-scale collection, TABLET establishes a foundation for robust training and extensible evaluation of future VTU models!
INTRODUCTION
The field of table understanding focuses on techniques for representing and interpreting tabular data support wide range of practical tasks such question answering, summarization, and information extraction. Research in this area has traditionally represented tables as structured text, encoding their content and layout through linearized or graph-based representations (see Figure [p: Herzig et al. 2220 Zhang et al. 2020 Liu et al. 2022). While this unimodal view remains effective certain domains, many tables found documents and webpages contain irregular structures, rely on visual formatting (e.g., merged cells, background colors, font variations), or embed multimodal elements such as images (see Figure [1). Advances in LanguaL Models (VLMs: Radford et al. 2021 Liu et al. 2023) have provided impetus for treating tables as images, eschewing the step of rendering them as text sequences (like Markdown or HTML).. The conceptual simplicity approach, coupled with improved performance on several tabular tasks (Alonso et al.) 2024 Zhou ctt al. 2025) has driven significant research interest (Zheng et al. 2024b| Su et al. 2024 Jiang al,, 2025 Visual Table Understanding (also known Multimodal Table Understanding). Visual representations of tables are not only merely convenient but in many cases necessary, particularly for VLM agents that interact with the world exclusively through pixels (e.g.. screen) and must interpret tables directly in their visual form (Deng et al. 2023 Zheng et al. 2024a Lu et al. 2024).
this
Despite the growing relevance of VTU, there are few resources that support training models directly on image-based representations of tables. Existing benchmarks like MMTab (Zheng et al.) 2024b) consist of web tables (e.g., from Wikipedia which is common source for many tabular datasets), that are serialized and subsequently rendered as synthetic images (see Figures [[.c). These images do not preserve the original visual characteristics of the tables and do not reflect the diversity and
Dataset, code, and other resources available wttthh:t//////// http::///iittuub.co conb. onscapp/TAHl.III

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Driginal Table
M
Visual Understanding Datasets
Rendared from
Table:
(C)
TABLET
1
Tittle voaridecca
Retricved from original scurce
examples
Suw was the
2M table images
[. with viaaliiiii
21 tabular tasks
soee Lect
(a)
(b)
(6)
Figure 1: Previous datasets render table images from serialized tables, losing original visual details. In contrast, TABLET locates and retrieves the original table visualizations across 14 tabular datasets, resulting in 4M examples grounded in 2M unique tables.
complexity of real-world layouts. As a result, models trained on such data face train-test mismatch, since the visual patterns learned from serialized renderings do not generalize well to naturally occur- ring tables failing to capture critical visual cues like subtle ruling lines, intricate merged cell layouts, background colors, font variations, or embedded images that are inherent to real-world table com- prehension (compare Figure T and [e). An exception WikiDT Shi et al. (2024), which provides access to original table visualizations from web pages but focuses exclusively on table question an- swering. Likewise, datasets designed for processing PDF documents or screenshots (Zhong et al., 2020 Lu et al. 2023) preserve original table visualizations but support single task.
desinedd to enable vision-language models to learn generalizable skills for table understanding by leveraging visual features from table images. Unlike existing datasets that focus on a single task or use synthetic renderings, TABLET preserves lossless representations of real-world table images and spans diverse set of tasks, including table- to-text generation (Parikh et al., 2020). table fact verification (Chen et al.) (020b)). and table-based question answering (Pasupat & Liang 2015). TABLET facilitates training on table formats that align with those encountered in downstream applications, particularly in pixel-based agents that process visual input directly. It contains million examples across 21 tasks, derived from million unique tables, 88% of which retain their original visualization. To promote flexibility and foster future research, we provide both image and HTML represettttt of tables, together with metadata and links to the source datasets. Pairing table images with HTML enables models to learn from naturally occurring and synthetic table images when the former are not available. TABLET'S large scale and task diversity make it foundational resource for research on integrating textual and visual modalities (Liu et al. 2025 Jiang et al. 2025).
In this work, we introduce TABLET large-scale dataset designed
To demonstrate TABLET's effectiveness as a training resource, we introduce VisualTableQA, Vi- sual Table Question Answering benchmark that pairs visually rich tables with questions that can- not be answered through table structure parsing visual cues alone, but demand joint reasoning over both modalities. Our experiments demonstrate that fine-tuming on TABLET yields substantial improvements VisualTableQA, as well as strong transfer to other in- and out-of-domain bench- marks. Our work makes the following contributions:
We introduce TABLET address critical gap in VTU datasets, providing 4 million ex- amples from million tables, most preserved in their original visualization. We design TABLET for extensibility with unified task format, HTML serializations that render back to original visualizations, and metadata with source links to facilitate reuse.
Supervised fine-tuning TABLET substantially improves VLM performance on both seen and unseen tasks. Our best-performing model achieves state-of-the-arr results on 11 out of 15 table understanding benchmarks.
We introduce VisualTableQA, a benchmark requiring joint table understanding and visual perception and show that fine-tuning on TABLET improves performance on this challenging unseen task.
?Our dataset is called TABLET partly as nod to the Scottish sugary confection which is often cut into uniform squares or rectangles, resembling table.
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2 RELATED WORK
Early work approached the problem of table understanding from a unimodal perspective, treating tables as structured text and developing general-purpose models that generalized across multiple tasks (Li al., 2023 Zhang et al., 2024). Recent progress in natural image understanding afforded by increasingly better VLMs (Radford et al. 2021; Liu et al. (023) has led to development of
models that can successfully process visually rendered text beyond natural imagery and perform
table understanding holistically, within a multimodal framework (Kim et a 2022
Lee et al 2023
Zhang et al. 2023 Ye et al. 2023; Hu et al.
2024, Alonso et al. 2024 Jiang et al. 2025 Su et al,
2024
Zhao et al. 2024 Zheng et al. 2024b
Su et al.) 2024).
To this effect, several benchmarks have been proposed recently to evaluate progress in VTU. For instance, Vob-le
(Kim et al. 2024) focuses visual table question answering but relies on synthetic table images and does not require visual reasoning: its questions can be answered from textual content alone. In contrast, MMTBench (Titiya et al., (025) includes original table visualizations, along with visually rich images and interleaved charts. While both are valuable for evaluation, they are limited in scope and do not provide large-scale training data, restricting their utility for developing generalizable models.
Larger training datasets have historically focused on image-based Table Structure Recognition (TSR), including PubTabNet (Zhong et al. 2020), TableBank (Li et al. 2020), and TabComp (Gau- tam al. 2025). Beyond TSR. Alonso et al. (2024) created image-rendered versions of existing datasets like ToTTo (Parikh et al. 2020), relying on lossy renderings that discard visual features. WikiDDT (Shi al. 2024) preserves original table visualizations but only targets question answer- ing. MMTab (Zheng et al. 2024b) is large VTU dataset, with 150k TSR pre-training examples and 232k instruction examples across 19 tasks. While MMTab is a useful resource that we build upon, it relies on synthetic renderings of serialized tables, lacks traceability to original data sources, and remains limited size compared to the scale needed for general-purpose VTU.
In this work, we do not introduce task-specific dataset model. Instead, we create a large-scale resource aimed at enhancing table understanding in general-purposs. vision-language models like Gemma-3 (Team al. 2025) Qwen2-VL VL (Wang et al. 2024), under the assumption that tables for many tasks are seen as images, and therefore can be naturally processed by these models. This distinguishes TABLET from many recent efforts that introduce task-specific datasets specialized architectures as it is designed to be a holist resource for advancing VLM capabilities for tables.
3
THE TABLET DATASET
3.1 OVERVIEW
TABLET is large-scale dataset that aggregates total of 4,066,851 examples, combining 21 TU tasks sourced from 14 datasets (see Section 3.3 for a breakdown of examples per task). TABLET includes 2,031,256 unique table images with visualizations from Wikipedia (61.5%), Pub-
TabNet (25.1 %;; Zhong et al. 2020). TabMWP (1.9%: Lu et al. 2023 and synthetically rendered images (11,5% from serialized tables (see Section 3.2 for details).
Examples in TABLET are framed as instructions, leveraging the benefits of instruction-tuning for large language models.TABLES TABLET does not introduce new training data instances; rather it repurposes examples from existing datasets, referred to as seed datasets, rephrasing their tasks into an instruc- tion format. Examples are drawn from 14 seed datasets: TURL (Deng al., 2020). ToTTo (Parikh) et al. 2020). TabFact (Chen et (0000)), WikiiTableQuestioons (Pasupat & Liang] 2015). HybridQA
(Chen et al. 2020b). HiTab (Cheng et al.) (022), PubTabNet (Zhong et al. 2020). TahMWP (Lu ctt al. 2023). TAT-QA (Zhu al. 2021). InfoTabs (Gupta et al. 2020). WikiBIO (Lebret et al.
2016). FeTaQA (Nan et al. 2022). MMTab Zheng et al. (2024b). and DocStruct4M (Hu et al,
2024 In addition, TABLET includes 306 newly created, human-annotater examples of questions based on visually rich tables drawn from six of the seed datasets.
Datasets that contain only images and fixed prompts restrict researchers to the provided visualiza- tions and instruction formulations, limiting the exploration of new prompting techniques or the use of table data create additional examples. To avoid this, each example in TABLET provided in
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Task
Sceds
Train
Dev
Test
ent_link
TURL
1,236,128 (35.3%)
74,282 (35.4%)
213,494 (60.8%)
cop_type
TURL
602,406 (17.25)
13,188 (6.3%)
12,802 (3.6%)
aruet_awwae-parse
M
513,482 (14.6%)
9,115 (4.3%)
1.102 (0.3%)
wikibio
WikiBIO
582,659 (16.6%)
72,831 (34.7%)
72,831 (20.7%)
hybridqa
HybridQA
62,670 (1.8%)
3,466 (1.7%)
3,463 (1.0%)
fetaqa
ToTTo
3,006 (0.1%)
577 (0.3%)
1,079 (0.3%)
hitab
NSE, StatCan, ToTTo
7,417 (0.2%)
1,670 (0.8%)
1,584 (0.5%)
infotabs
InfoTabs
16,538 (0.5%)
1,800 (0.918)
5,400 (1.5%)
tabfact
TabFact
87,717 (2.5%)
12,389 (5.9%)
12,326 (3.5%)
tabmwp
TabMWP
23,059 (0.7%)
7,686 (3.7%)
7,686 (2.2%)
tat-qa
TAT-QA
2,201 (0.1%)
278 (0.1%)
277 (0.1%)
totto
ToTTo
110,934 (3.2%)
7,077 (3.4%)
7,084 (2.0%)
wikitq
WikiTableeuusstions
14,152 (0.4%))
3,537 (1.7%)
4,344 (1.2%)
rel_extraction
TURL
60,615 (1.7%)
2,145
(1.0%)
2,030 (0.6%)
iable_instructtinn
M
136,944 (3.9%)
(0.0%)
0 (0.0%)
crolumn_extraction
M
7,721 (0.2%)
(0.0%)
957 (0.3%)
M
7,727 (0.2%)
(0.0%)
966 (0.3%)
abble.celllocatiee
M
7,708 (0.2%)
(0.0%)
956 (0.3%)
aublerrecognition
M
6,927 (0.2%)
(0.0%)
912 (0.3%)
table_size .detection
M
7,800 (0.2%)
(0.0%)
950 (0.3%)
detecctioo
M
7,500 (0.2%)
(0.0%)
950 (0.3%)
visual.table.gg
M
(0.0%)
(0.0%)
306 (0.1%)
Total
3,505,311
210,041
351,499
Table 1: TABLET splits, tasks, and seed datasets; M InfoTabs, NSF, StatCan, TabMWP, TAT-QA, TabFact, ToTTo, WikiBIO, WikiiTableQuesiions; M InfoTabs, NSF, StatCan, TAT-QA, TabFact, ToTTo, WikiBIO; M PubTabNet, TabFact, WikiTTableQuestions;; M ToTTo, HybridQA, TabFact, WikiBIO, TURL, WikiTTaleeQuesiios.
unified format, including the instruction used in this work, its corresponding atomic data, the HTML. version of the table, the original source ID, and additional metadata (detailed in Appendix7.4).
3.2 IMAGE SOURCES
Wikipedia Tables Wikipedia tables generally follow hierarchical structure and are often rich in vi- sual information. 61.5% of the tables in TABLET are lossless visualizations sourced from Wikipedia (see Appendix [7.2 for a breakdown of image sources per task). These tables are referenced in 67.4% of all examples in the dataset. Each table from the seed datasets traced back to its original visualization in the corresponding Wikipedia article and captured in a screenshot [ A key challenge in retrieving the original visualizations was that the seed datasets were created different times, while Wikipedia articles are continuously updated. To address this, we relied on the meta- data released with the seed datasets and also reached out to their authors find out when the tables were harvested (see Appendix 7.3). We used Wikipedia's 's archiving API to retrieve each article as it appeared at the time of crawling. All Wikipedia tables in TABLET are linked their source via the page identifier and the corresponding revision identifier (oldid). As Wikipedia pages often contain multiple tables, we identified which one corresponded to the seed example by computing the Leven- shtein 1966) edit distance between the tables represented in markdown format, and selecting those with the highest similarity value. We set a minimum similarity threshold of 0.7. In cases without a match, the serialized seed table was rendered
image.
Synthetic Tables Wikipedia tables that could not be retrieved, either due to low similarity scores or other issues, were synthetically generated by converting their serialized format to HTML and rendering them using the same browser configuration used for the original Wikipedia tables. These synthetic tables account for 11.5% of the dataset, contributing 747,002 additional examples (18.4% of the total). In Section 5 we show that combining original table images with synthetic visual-
'Table images were rendered using Firefox in headless mode (version 142.0.1 with GeckoDriver version 0.36.0) effective rendering density of 96 PPT (which stands for Pixels Per Inch).
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izations during training leads to improved performance over using either alone, due to increased example count and visualization diversity.
Document Tables We also incorporate tables from other domains to improve model generalization. These include tables from scientific articles in PubTabNet (Zhong et al. 2020) and rendered tables from TabMWP (Lu et al., 2023). PubTabNet contributes 509,892 tables to dataset (25.1% of the total), accounting for 97.5% of the structure-awar learning task incorporated from DocStruct4M (Hu al. (024). TabMWP adds 38,431 tables (1.9% of the total); while smaller in scale, TabMWP
offers valuable coverage of numerical reasoning examples.
3.3 TABLE UNDERSTANDING TASKS
TABLET aggregates a total of 20 TU tasks grouped into 7 broad categories: Table Interpretation, Question Answering, Table-to-Tex Generation, Table Numerical Reasoning, Table Natural Lan- guage Inference (NLI), and Table Structure Understanding. Subtasks within each category require different skills and demonstrate various degrees of complexity, ranging from basic table structure understanding to downstream tasks that combine tabular reasoning with information retrieval, nu- merical reasoning. or natural language inference. [able[[ a breakdown of examples per task and their source dataset. We briefly describe these below and provide more details in Appendix 7.5
Table Interpretation comprises three tasks, namely Column Type Annotation, Entiy Linking, and Relation Extraction (ent_link, col_type, and rel rel_extra in Table [ Sourced from TURL (Deng) iet al.] 2020), these tasks do not represent downstream applications but target basic table understand- ing skills essential for tackling more complex tasks. All instructions for these tasks were aggregated from TURL. They constitute the largest group in our training set, with 1,899,149 examples (54.2%).
Table Question Answering is represented by Free-form Table QA (FeTaQA, Nan et al. 2022 Hierarchical Table QA (HiTabQA; Cheng et al. 2022). Hybrid QA (HybridQA: Chen et al. 2020b), and Table QA WikiTableeQustionss Pasupat & Liang 2015). These tasks range from simple question answering over table content to multi-hop reasoning that combines textual and tabular information. They also vary in terms of table complexity and the expected answer format. Question answering represents 2.5% of TABLET's training set (87, 187,245 examples).
Table-to- Text Generation is exemplified by two subtasks, namely Highlighted Cell Text Generation (ToTTo: Parrkkh et al. 2020) and Wikipedia Biography Generation (WikiBio; Lebret et al. 2016). Both tasks involve generating text based on table content, concise biographies in the case of WikiBio, and short descriptions based on visually highlighted cells, in the case of ToTTo Together, they account for 693,593 training examples (19.8%).
Table Numerical Reasoning combines Tabular Math Word Problem Solving (TabMWP; Lu et al. 2023) and Hybrid-context Financial QA (TAT-QA: Zhu et al. (02T). Given a table and a mathemati- cal question, the model must perform reasoning over table values. This type of numerical reasoning represents 0.8% of TABLET'S training set (25,260 examples).
entailment tasks: Table Fact Verificatiin (TabFact: Chen et al. 2020a) and Infobox Natural Language Inference (InfoTabs:; Gupta et al. 2020)) where statements as supported or refuted based on table content. They represent 104,255 examples (3%) in TABLET'S training set.
Table NLI includes
Table Structure Understanding is an umbrella category for tasks designed to facilitate structural understanding of tables: Merged Cell Detection, Row and Column Extraction, Table Cell Extrac- tion. Table Cell Location, Table Recognition, Table Size Detection, and Structure
Parsing in Table D. Examples for the first seven tasks are aggregated from MMTab, while Structure aware Parsing is derived from DocStruct4M (Hu et al.) 2024). These tasks are based on tables from mul- tiple seed datasets (see rows with seeds M M and M³), including InfoTaas (Gupta et al. 2020), NSF (National Center for Science and Engincering Statistics 2019), StatCan (Statistics Canada 2024). TabMWP (Lu et al. (2023). TAT-QA (Zhu et al. 2022). TabFact (Chen et al.] 2020a). ToTTo (Parikh et al. 2020). WikiBio (Lebret et al. 2016). WikiTabbeeQusttnn
(Pasupat & Liang 2015)
and PubTabNet Zhong et al. (202)). While we maintain the original MM instructions, we use own table visualizations. This group contributes 558,865 examples (15.8%) to our training.
Instruction Following To facilitate evaluation, we fine-tune and evaluate all models using instruc- tions that explicitly require the final answer be encapsulated in JSON object, regardless of
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any additional tokens the model may generate. To increase instruction diversity and mitigate catas- trophic forgetting, we include a dedicated instruction-following set from MMTab. These instructions rephrase subset of examples from the above tasks using different template and do not require the ISON output format. While not a task in itself, this set adds instruction diversity and reduces over- fitring single output style. We aggregate these examples directly from MM while using our visualizations. This set contributes 136,944 training examples (3.9%).
Visual Table Question Answering Although many tasks in TABLET involve visually complex tables, they do not explicitly require models to exploit visual features beyond textual content. To evaluate whether training TABLET's original images improves models' ability integrate visual perception with table understanding, we introduce a manually curated Visual Table Question An- swering benchmark. Human annotators selected tables with high visual complexity, measured by our visual complexity scorer (Section | and Appendix (0), and formulated questions answerable only through joint reasoning over visual cues and tabular structure, e.g., "What model is the red car in the table?" or "In what year did the king holding red cross die?". To ensure questions genuinely require visual reasoning, we filtered out questions solvable from lossy synthetic represen- tations alone. The resulting benchmark comprises 306 examples, each annotated with a question type to enable fine-grained performance analysis (see Appendix 13 for taxonomy and examples).
3.4 LINKING TO SOURCES AND HIGHLIGHTING
TABLET is designed with extensibility in mind. Existing datasets such as TableInstruct (Zhang et al. 2024), DocStruct4M (Hu et al., 2024), and MMTab (Zheng et al.] (024b) do not provide a clear reference to the original examples from which their instructions were derived. However, the absence of such pointers, makes reuse, modification, and augmentation difficult. Additionally, image-based datasets such as MMTab lack serialized table versions in text format, providing only rendered images as representations. To address these issues, each example in TABLET includes both the identifier of the original example and the identifier of the corresponding table from the source dataset. Identifiers follow a simple, human-readable format, and the released code provides function to retrieve the original examples given their IDs. To support future extensions, TABLET also provides tables scrialized in HTML. For tables retrieved from Wikipedia, this corresponds to the raw table object from the article's original HTML, which can be rendered using Wikipedia's official CSS stylesheets or the rendering functions included in our code. All remaining tables are either provided as HTML in the seed datasets or converted into HTML from their serialized forms.
This feature is particularly useful in tasks involving cell pointing or highlighting. Prior work has shown that models can achieve competitive results by relying uniquely on highlighted values (An) et al. 2022; Alonso et al. 2024). We also observed in our experiments that as long as highlighted cell values are mentioned in the prompt, models are able to perform the task (e.g., Column Type Annotation, Entity Linking, Relation Extraction, FeTaQA. and ToTTo) regardless of the table pro- vided. We mitigate this, by exploiting the HTML scrialization to directly locate the highlighted cells in the source tables. By cross- cross-referencing the highlighted spans from the seed datasets with the actual table content, we generate versions of the tables with explicit highlights while preserving the original visual features.
3.5 COMPARISON WITH MMTAB
MMTab (Zheng et al. 2024b) is also a dataset for visual table understanding: however, unlike TABLET, it does not include the full set of training examples from the original datasets (c.g. only 12.4% of ToTTo compared to our 91.8%; see Appendix 7.66, and it provides no development splits. MMTab covers 20 tasks while TABLET covers 22 (inculding instruction tuning); four tasks are unique to the former and five to the latter, and overall our dataset is 9.38 times larger (compris- ing 4,066,945 examples compared to MMTab's approximate 433,376 examples). Moreover, only few tasks in MMTab include identifiers that can be linked back to their source examples, limiting developers the provided instruction text. While MMTab relies synthetically rendered tables with predefined styles, which omit original formatting and embedded images, TABLET retains the original visualizations, enabling models to exploit fuller range of visual features.

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WikiBio
ToTTo
WikiTQ
HiTab
TabFact
FeTaQA
Infotabs
DegScore
Model
org
org
A
org
A
org
org
A
org
org
total
0-Shot
2.5
+0.5
8.9
-1.9
50.8
-6.9
2.5
-8.6
59.4
-11.0
9.8
-0.1
64.5
-4.6
-28,90
TABLET-B symhh
4.8
-6.5
14,4
+2.0
56.4
-1.9
16.0
-17.5
65.5
+2.4
27.9
51.8
-0.6
-22.35
TABLET-Barz
11.5
+1.9
15.9
+2.2
56.2
-2.3
24.7
-10.4
63.7
+1.6
28.4
0.0
48.7
-2.7
-6.63
TABLET-B -
12.0
+1.7
14.1
+1.6
56.5
-2.4
26.9
-11.6
70.0
+1.8
28.5
-0.1
55.5
-4.8
-7.87
models fine-tuned on different TABLET-B vari- ants and in zero-shot mode. We report performance when evaluating on original (lossless) table visualizations across held-in and held-out benchmarks and the corresponding degradation (A) rel- ative to evaluation on synthetic (lossy) tables. Negative A values indicate worse performance original visualizations. We report exact match accuracy for WikiTQ. TabMWP, TabFact, and TAT- QA; BLEU for WikiBio, ToTTo, and FeTaQA;; and F1 for HiTab (ToTTo on the dev set). Best results per setting (synth/org) are shown in bold. DegSc denotes the normalized aggregate degradation, computed separately for BLEU, accuracy, and Fl (see Appendix 8.2)
Table 2: Comparison of Qwen2.5--V--7B-nnstee
4 EXPERIMENTAL SETTING
Our experiments are motivated by three questions: (1) Does training with original visualizations lead to more robust generalization real-world tables, even on benchmarks that do not explicitly require visual features, or does the added visual information act as noise and degrade performance?; (2) Does fine-tuning on TABLET outperform existing resources on both seen and unseen VTU tasks?; and (3) Does fine-tuning on TABLET enable models to better integrate visual perception and table un- derstanding for unseen Visual Table Question Answering tasks? We also examine whether training only with original visualizations performs better than mixing original and synthetic ones, whether balanced task distribution is preferable to the full dataset distribution, and whether including various table interpretation tasks contributes to VTU performance.
Backbone Model All main experiments employ the same backbone model, namely Qwen2.5-Ve with 7B parameters (Qwen et al.) 2025). This model provides good tradcoff between perfor- mance and computational requirements. Together with InternVI.3 (Zhu et al. 2025). it was the best-performing open-weight VL.M in its class, setting a new benchmark for Single Page Document VQA (Mathew et al., 2022l at the time of writing, Exploring the best backbone model for VTU or advanced SFT strategies is outside the scope of this work. Full SFT configurations are detailed in Appendix results for other open-weight models on TABLET (test set) are reported in Appendix 8.3
Evaluation We evaluate fine-tuned models on eight held-in tasks: three focus on Table QA (Wik- iTableQuestions, HiTabQA, FeTaQA), two on text generation (WikiBio, ToTTo), two on Table Nu- merical Reasoning tasks (TabMWP, TAT-QA), and one on Table II.I (TabFact). To test generaliza- tion, we further evaluate on seven held-out tasks: VisualTaaleeQa the new benchmark introduced in this work, HybridQA, Inbo, TabMCQ (Jauhar et al. 2016), AIT-QA (Katsis et al.) (022)). Ta- ble Recognition, and PubHealth (Akhtar et al. (022)). The last four are directly extracted from MMTab and use instructions generated with their own templates, adding diversity in both table and instruction styles. Although included in TABLET, the 86,135 training examples from HybridQA, InfoTabs, and Table Recognition were excluded from fine-tuning to enable held-out evaluation.
5 RESULTS AND ANALYSIS
Lossless vs Synthetic Table Visualizations We assess whether visual information present in loss- less, real-world table visualizations improves model robustness. We conduct this experiment on a subset of TABLET corresponding to MMTab, the current state of the art in multimodal table un- derstanding, which we refer to as TABLET-BASE (TABLET-B). We create three training variants: (1) TABLET-Byyth uses only synthetic table renders (238,980 examples); (2) TABLET-B replaces all Wikipedia- bassed images with original visualizations while keeping tables without lossy serial- ization unchanged (238,980 examples); and (3) TABLET-BII extends TABLET-Ban with synthetic renders for tables whose original visualizations could not be retrieved (371 1971,992 examples; 43.5% original, 35.6% synthetic, 20.9% unchanged). We perform full fine-tuning (without LoRA) on
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Held-in Datasets
WikiBio
ToTTo
WikiTQ
TabMWP
HiTab
TabFact
FeTaQA
TAT-QA
0-Shot
2.5
9.1*
53.4
59.1*
31.2*
73.9*
7.0"
6.9
1-Shot
4.4
16.1
49.8"
57.7"
37.3*
72.8*
8.5"
10.1
Qwen2.5-V
MMTab
6.4"
12.6
48.7*
80.4"
41.5*
57.0"
1.7
9.4"
TABLET-S
2.9
28.9
56.8
84.0
64.8
78.9
28.7
27.8
TABLET-M
3.1
29.3
56.6
84.0
67.0
79.5
30.7
31.0
TABLET-L
3.8
30.4
55,5
84.5
67.5
79.5
31.5
32.5
Gemma 3
0-Shot
4.1
4.9
35.1
65.2
20.6
62.1
20.5
7.9
MMTab
4.6
18.1
29.7
73.2"
7.6
56.6
21.3
6.9
TABLET-M
13.2
28.2"
33.1
71.8
62.8"
60.0
34.0"
11.2
TABLET-
13.3
25.0
29.2
68.8
43.0
59.5
28.7
10.8
in 0/1-shot mode and fine-tuned on MMTab and different TABLET sizes. Results for Gemma 3 4B in 0-shot mode and fine-tuned on MMTab ,TALLETM, and TABLET-L using LoRA are also Iecluded.Resulfe for ToTTo correspond to evaluation on the development set. We report exact match accuracy for Wik- iTQ, TabMWP, TabFact, and TAT-QA; BLEU for WikiBio, ToTTo, and FeTaQA; and F1 for HiTab. Best performing model per task is shown in bold; we mark with * models sigmificantly different (p 0.05, using bootstrap resampling) from those trained on TABLET (highlighted in gray). Mod- els that perform significantly better within the TABLET group are underlined. Results for other geight models on TABLET (test set) are reported in Appendix 8.3.
Table 3: Evaluation on eight held-in datasets with Qwen2.5-VI-77-Innste
Qwen2.5-VL-77 VL-7BB for 3 epochs and evaluate on six held-in tasks and one held-out task, using both original and synthetic table images simulate diverse vaining-evaluaiie conditions.
Table 2 shows that training on original visualizations substantially improves robustness to visual variation. The zero-shot baseline degrades by 28.9 percentage points when evaluated origi- nal rather than synthetic tables, particularly on HiTab (-8.6) and TabFact (-11.0). Training on TABLET-B reduces this degradation 22.35 points, but models still struggle with real-world visualizations. In contrast. TABLET-Borg achieves the lowest degradation (6.63 points), demonstrat- ing that exposure original visualizations during training is crucial for robustness.
Comparing absolute performance on original tables, TABLET-Borg outperforms TABLET- on four tasks (WikiBio, ToTTo, TabFact, FeTaQA) and matches on WikiTQ, underperforming only on HiTab and InfoTTbbs. The mixed training variant TABLET-Bmm. achieves the best overall results, winning on five of seven tasks (WikiBio, WikiTQ, HiTab, TabFact, FeTaQA) while maintaining low degradation (7.87 points). This demonstrates that combining original and synthetic visualizations leverages both the realism of original tables and the additional scale from synthetic renders, yielding models that are both accurate and robust across visualization styles.
Performance on VTU Tasks with TABLET We next evaluate whether TABLET improves perfor- mance seen and unseen VTU tasks. While fine-tuned models are expected to perform bet- ter on tasks seen during training, improved performance on held-out datasets would suggest that training TABLET enhances the model's VTU capabilities. Tables 3 and 4 report on held-in and held-out tasks, respectively. We present comparisons between zero- and one-shot Qwen2.5-VLL-7EE and its fine-tuned instantiations trained on MMTab and different sizes of TABLET. For MMTab, all Wikipedia-sourced images are synthetic (61.8% of all tables in their dataset), whereas all TABLET versions use mix of original (88%) and synthetic (12%) (See Appendix 7.2 for the distribution of image sources per task). For now, we focus solely on TABLET-L (a shorthand for TABLET-LARGE), which is our biggest dataset comprising 4M examples (we discuss smaller sizes in the next sections).
results
We observe that the model fine-tuned on TABLET-L performs significantly better on held-in and held-out datasets. It dominates in all QA benchmarks except HybridQA, where long-context profi-
We should note that Qwen2.5-VI--7B VL is competitive baseline that has undergone extensive pre-training and instruction-tuniin -tunin, achieving top in many TU and Visual Document Understanding benchmarks.
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SAURY
-Dilassst- AIT-QA PubHealtiTab HybridQA TabRec
183
Infotabs TabMCQ
VTQA
0-Shot
64.5"
84.4
51.7*
64,7"
34,2"
24.5"
42.4"
MMIlb
56.9"
63.9"
43.6"
41.1"
TABLET-S
57.2
88.2
58.9
64.7
22.8
43.9
45.2
TABLET-M
61.5
62.4
66.3
27.7
TABLET-L
6144
87.9
70.8
70.2
25.1
45.4
45.6
0. TABLLTT-
b. TABLET-L
Table 4: Left: Evaluation on five held-out datasets with Qwen2.5- VL-7B-LL-IILee (0-shot) and fine-tuned on MMTab and different TABLET sizes. We report Tree-Edi Distance-based Similarity for Table Recognition (TabRec) and accuracy for all other benchmarks. HybridQA results are re- ported on the development set. Best performing model per task is shown in bold; we mark with models significantly different (p < 0.05, using bootstrap resampling) from those trained on TABLET (highlighted in gray). Models that perform significantly better within the TABLET group are under- lined. Right: Distribution of examples per task in TABLET-L (b) and TABLET-M (a).
ciency is likely reduced since its training set was excluded from fine-tuning Notably, in tasks such as HiTab, FeTaQA, and TAT-QA, where TABLET includes fewer training examples than MMTab, the model still performs considerably better, suggesting benefits from transfer across tasks, original visualizations, or simply due to a higher-quality subset of examples. Finally, tasks that include hi- erarchical table structures such as ToTTo and HiTab show clear gains. Table cell hierarchy is not lost in synthetic tables, but is often conveyed more clearly in their original visualizations. Our re- sults are encouraging, particularly when considering that portions of the held-out datasets, including test sets, may have been encountered during pretraning. Models trained on TABLET outperform 0/1-shot Qwen2.5-VL-7B on 12 out 14 (held-in and held-out) tasks. Interestingly, our gains extend beyond unseen tasks. For instance, ToTTo, HiTab, TabFact, FeTaQA, and TAT-QA contain the same number of training examples across all TABLET variants, yet performance consistently improves as additional tasks are incorporated. This suggests that fine-tuning on a broader task set facilitates transfer learning, yielding improvements even on tasks where the model was already competitive.
Optimal TABLET Size Since training on 4M examples is resource-intensive, and tasks in TABLET-L exhibit substantial variation size (see (b) pie chart Table 4). we evaluate whether a smaller but more balanced dataset might perform comparably. We create TABLET-M (a short- hand for TABLET-MEDIUM) by capping each task 140k examples. For benchmarks exceeding this cap (Column Type Annotation, Entity Linking. WikiBio), random 140k subset is sampled and included as representative for that task. This results in a dataset with 1,117,217 training examples. We exclude HybridQA, InfoTabs, and Table Recognition from the training set to allow for held-out evaluation, leaving 1,031,082 examples in TABLET-M and 3,419,176 in the full dataset of our ex- periments. The capped distribution is shown in pie chart (b) and Table 4 (further details on training sets are in Appendix 7.I).
Models trained TABLET-M perform competitively and in some cases, even better than TABLET-L (see Table 4 rows in gray), offering a compelling trade-off between dataset size and model effectiveness. That said, the full dataset still yields slightly better overall performance when fully fine-tuning larger model and remains a valuable resource for future work that can benefit from large-scale, lossless training.
Robustness Across Models and Training Regimes We observe similar performance trends when training Gemma 4B with LoRA (Table 3 Appendix 12). Fine-tuning on TABLET-L yields sub- stantial gains over the zero-shot baseline on six of eight tasks, with the largest improvements on visually rich datasets like HiTab (+22.4). Notably, both TABLET-M and TABLET-L significaal out- perform MMTab on WikiBio, ToTTo, HiTab, and FeTaQA, despite using parameter-efficient LoRA A rather than full fine-tuning employed for Qwen2.5-VI. This demonstrates that TABLET'S benefits robust to model architecture, model scale, and training methodology.
The Benefit of Training on Table Interpretation Tasks Table Interpretation tasks represent 54.2% of the training examples in TABLET-L, making them the largest category by volume and the second longest in instruction length, following HybridQA (see Appendix I.I). While Deng et al. (2020) demonstrated the benefits of these tasks for textual TU, it is unclear whether this carries over to VTU. If these tasks prove unhelpful, removing them would save significant resources. We therefore remove all Table Interpretation tasks from TABLET-M and fine-tune on this smaller version (we use
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O-Shot
MMTab
TABLET-S
TABLET-M
TABLET-L
PetaCe
1ITT
UM0
10
10111
20-
Figure 2: Model (Qwen2.5- performance acToss different levels of table visual complexity (x axis). Higher levels correspond to more visually complex tables. Complexity levels are comparable across tasks, e.g., level represents the same complexity range in every benchmark.
TABLET-S as a shorthand for TABLET-SMALI which excludes any tasks related to Column Type Annotation, Entity Linking, and Relation Extraction resulting in dataset with total of 690,467 training examples (67% of the example count in TABLET-M). As shown in Table there are benefits to be gained from including these tasks. Models trained TABLET-M outperform those trained on TABLET-S on six out of 8 held-in benchmarks (Table 3 and achieve comparable performance on the remaining two. Similar gains are observed in four of five held-out tasks (Table 4. These
findings demonstrate that the benefits observed in the textual domain Deng et al. 2020 extend to multimodal VTU.
Performance on Unseen Visual Table Question Answering Table 4 also reports results on Vi- sualTableeA (VTQA), our benchmark designed evaluate models' ability jointly reason over visual cues and tabular structure. Fine-tuning TABLET yields substantial improvements over both zero-shot (42.4%) and MMTab (41.1%) baselines, with TABLET-M achieving the best perfor- mance (47.8%). This represents a 13% relative improvement over zero-shot and 16% over MMTab, demonstrating that exposure to TABLET'S visually rich original table images enables models to de- velop compositional skills that transfer to this challenging unseen task. Notably, these gains emerge without any task-specific VisualTableeQA examples in the training data, confirming that TABLET'S diverse task mixture and authentic visualizations foster generalizable visual reasoning capabilities. Results are averaged over three random seeds and reported as exact match accuracy.
Visual Complexity Analysis We hypothesize that TABLET is especially effective for visually com- plex tables; those deviating from regular, monochromatic layouts toward richer structures with stylistic cues, embedded images, irregular formatting, and diverse styling. These tables suffer most from serialization and motivate our work. Since TABLET contains both format-aware serialized rep- resentations (e.g., HTML) and rendered visual forms, we compute a complexxyy score aggre- irregularity, visual entropy, font diversity, embedded image
gating factors such as row/column-speee features, and edge irregularity (see Appendix [o] details). We rank tables by this score and group them into 20 equally spaced bins for analysis, omitting bins with fewer than 5 examples for clarity. Figure shows results for tasks with the most pronounced model differences (see Appendix [T for all benchmarks). Across these tasks, models trained on TABLET remain robust visual complexity increases. For tasks like TABMWP, fine-tuning on table images proves crucial to avoid performance degradation, with TABLET-trained models showing consistently higher performance overall.
CONCLUSIONS
In this work, we introduced TABLET, large-scale dataset for Visual Table Understanding that ag- gregates over 4 million examples across 21 tasks and 2 million unique tables. Unlike prior resources, TABLET preserves original table visualizations whenever possible, provides both HTML and image representations, and maintains full traceability to the source datasets. Through extensive experi- mentation, we showed that (I) training TABLET improves model robustness when evaluated on real-world tables compared to synthetic renderings; (2) models fine-tuned on TABLET consistently outperform those trained on existing VTU datasets across both held-in and held-out tasks; and (3) TABLET brings improvements on unseen benchmarks, including the VisualTableQA benchmark in- troduced in this work, which suggests that its diversity supports transfer across VTU tasks.
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ACKNOWLEDEGMENTS
We thank the anonymous reviewers for their feedback and Miao Li for useful discussions. We gratefully acknowledge the support of the UK Engineering and Physical Sciences Research Coun- cil (grant EP/W002876/1 I), the Ministry of Science, Innovation, and Universities of the Spanish Govermment MCIN/AEI/1O.. 11110.13333 13039/501 10001 1033 by means of the projects: (i) MOLVI (PID2024- 1578550B- C32) and by FEDER, EU; (ii) HumanAAze (AIA2025- 19922--63322-661) the CHIST-ERA funded by the Ministry of Science, In- novation, and Universities of the Spanish Government (PCI2025-1) (120025--63866) 162286,, the Basque Government (IXA excellence research group IT1570-22 and IKER-GAITU project), and the European Union under Horizon Europe (Project LUMINOUS, grant number 1357244.
grant (Project Geo-R2LLM,
SHISSTERAA-2-MuttiiI
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7 APPENDIX
7.1 TABLET TRAINING SET STATISTICS
While the full released version of TABLET includes all available data, experiments used different partitions. Specifically, to allow for held-out evaluation, we excluded HybridQA, InfoTabs, and Table Recognition from the training set; in addition, created three training sets varying in size (TABLET-L, TABLET-M, TABLET-S) to explore various research questions. The statistics for these The development and test sets remain identical to the ones
training sets reported in Table | mentioned in the paper.
Examples
Tasks
Original Images
TABLET-S 690,467
14
521,032 (75.5%)
TABLET-M
1,031,082
17
812,748 (78.8%)
TABLET-L
3,419,176 17
2,795,485 (81.8%)
Table 5: Training set statistics for TABLET-L, TABLET-M, and TABLET-S used to fine-tune Qwenn2.5-VL 7B in our experiments. We report the number of examples (Examples), the tasks used (Tasks), and the number of examples with original table visualizations.
7.2 IMAGE SOURCE PER TASK
Figure shows the distribution of images per task included in TABLET. Images are sourced from Wikipedia, TabMWP, PubTabNet or are syntehtically rendered from information the seed dataset.
7.3 DATASET CRAWLING DATES
We determined data collection dates for each dataset through vari- ous sources and methodologies. TURL originates from the TabEl dataset (Bhagavatula al. 2015), which crawled from the November 2013 English Wikipedia dump. For ToTTo, we ob- tained the collection date through direct correspondence with Ankur Parikh. TabFact's ' timeline was established based on email corre- spondence with Wenhu Chen and the initial arXiv publication date.
Dataset
Date
TURL
2013-11-01
ToTTo
2019-03-01
TabFact HybridQA 2020-01-31
2019-06-30
Infotabs 2019-10-10
For HybridQA, we conducted a comprehensive analysis of all dates present within the dataset tables and observed a significant decrease in data frequency from February 2020 onward, indicating the col- lection cutoff point. InfoTabs' crawling year is documented at the bottom of their GitHub repository page, though the specific day represents our estimation. WikiBio's 's GitHub documentation indi- cates that their tables sourced from the Common Crawl snapshot enwiki-20150900
WikiBio
2015-09-01
Table 6: Data collection time- line for benchmarks included in TABLET.
7.4 DATASET EXAMPLE
For reference, in Figure we show an example from TABLET for the ToTTo table-to-text task. Note that examples for every other task follow the same format. We provide the instructions used in our experiments, together with the corresponding example metadata, and links to the corresponding example and table from the source dataset.
7.5 DEFINITION OF TASKS REPRESENTED IN TABLET
As mentioned Section 3.3 TABLET includes 21 TU tasks. We provide more detailed descriptions for each below.
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col.type type
ent.link
Telaqu
hitab
hybridaqa
80.7
76.2
14.8
90.6
90.3
19.3
9.4
85.2
9.7
infotabs
merged cell
relextraction
row www.omoonnnncmm extraction
stuetawwarc.aans
51.5
30.8
85.3
97.6
1.2
69.2
14.7
11.3
tabfact
table_cell lele_ceel__eeee Lextraction
well location
jablejusttuucton
taboonrooognition
45.5
53,9
50.5
44.3
38.7
17.6
17.6
13.0
29.8
54.5
28.4
31.9
42.7
tazee_size deteeciooo
wikibio
wikitg
tat
49.0
88.5
91.1
100.0
8.9
68
27.0
24.0
labrwp
visual table
Total
67.4
Wikipedia
Synthetic
100.0
100.0
12.6
TabMWP
PubTabNet
Figure 3: Image source distribution in TABLET, broken down by task. That is, source of the image referred by each example in each task. While the distribution resembles that the unique image pool, it is computed the example level (e.g., if the same Wikipedia image appears in two examples of a task, counted twice). Wikipedia are original visualizations form Wikipedia. Seed render synthetic images rendered form information in the seed dataset.
Column Type Annotation In this task, the model is required identify the data type of the val- ues highlighted table column. The model selects from a set of 255 randomly shuffled candidate data types. There can be multiple correct types per column. The column is visually highlighted to ensure that the model attends to the table itself rather than relying solely on the textual instruc- tion to infer the answer. Examples for this task were obtained from TURL. Our dataset includes 602,406/11 33, 188/12,802 (train/dev/test) instructions for this task. An example of this task is shown in Figure 18
Entity Linking For this task, the model is given a table with visually highlighted cell along with up to 100 candidate entities and descriptions and must identify which entity and description corresponds to the entity in the selected cell. This task was aggregated from the TURL. dataset. Our dataset includes 1,236, 088774,882213,444 (train/dev/test) instructions for this task. An example of this task is shown in Figure 6
Relation Extraction This task requires the model to select appropriate relations between two visually highlighted columns of table from set of candidates. This task is derived from the
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Table Numerical Reasoning (TabMWP, TAT-QA) Given a table and mathematical question, the model must answer using mathematical reasoning over table values. Instructions were based on examples from TabMWP and TAT-QA Our dataset includes 23,059 / 7,686 / 7,686 for TABMWP (train dev test) examples. An example of one of these tasks shown in Published as a conference paper at ICLR 2026
"exorple id"; "6o0rAiledd/AAaallo////eee2
hobble.htnl.... http:///w.a../aa///o//o////////a http:///eeeatttaaaooeeeeeeeeeeee "toble ino wwtp:///wwwaaaaaa/////////////aa htt.://ww.....a.....o...//...o.r
"toble raw html path":
single-sentence sursory focused http:///oooooootooto////////////
iaale__innoooopattoo "ing_source", " "Wikspecta".
365c12. png":
"irput": 'Taking into occount the table information 'List of telenoveles Rede Globo". '2002s",
hight iightd calls Format thin following onject (V"answerX"" VYYOU
AMSMERV")"",
"output";; "EV"onswer"": VA Favorita is the telenoveto atred in the 9 pn simestot.
"ruttdota":: ["Haaa__eetence"" Leentence" ("idx"; [14, 69], "in": "output"), "table_nage_titlee {...), Leble_seed_ie" 11822885236448822.111.:..:
"split"; "troin",
"src_oxarple_dde"
"ToTTo": "1762325566664002
"table_id": 466
"toble page title":
"List of 8/9 PM telenovelas of Rede Globo".
"2080s".
"tahl_ssee_dotosstt" "TolTo":
varient": "nighlightsd""
"tosk": "totto", wible_wikt. pogepeo_""""" "45544626", "toble wiki old id": 876845524,
Figure 4: Example of a TABLET example for the ToTTo table-to-text task.
TURL dataset. Our dataset includes 60.61502,.45/2,0300 (train/dev/test) instructions for this task. An example of this task is shown in Figure 8
Structure Aware Parsing In this TSR task. the model needs to parse the table into markdown format. This task comes from Docstruct4M, using tables from PubTabNet, TabFact, and WikiTable- Questions. Our dataset includes 1/3,482/9,115/1,0 (train/dev/test) instructions for this task. An example of this task is shown in Figure 10
Free-form Table Question Answering (FeTaQA) In this task, the model generates free-form an- swers to questions about Wikipedia tables, often requiring integration of information from discontin- uous sections of the table. Unlike datasets with shorter text spans, FeTaQA emphasizes higher-Ievel understanding through long-form answers. Examples for this task come from FeTaQA dataset. Our dataset contains 3, 3,000/577/11077 (train/dev/test) instructions for this task. An example of this task shown in Figure 7
Hierarchical Table QA (HiTabQA) This question-answeriig task involves hierarchical tables (with different headers across the table) and sometimes includes numerical reasoning, such as sums, averages, maximum, minimum, and counting, among others. Examples for this task were obtained from HiTabQA. with tables from ToTTo, StatCan, and NSF seed datasets. Our dataset contains 7,417/1 01111,67011,566 1,884 (train/dev/test) examples. An example of this task is shown in Figure
Table Fact Verification (TabFact, Infotabs) Also known as Table Entailment, this task involves classifying statements as supported or refuted based on table content. Examples for these two tasks come from TabFact and Infotabs seed datasets. Our dataset includes 87,717/ 12,389 12,326 (train / dev / test) TabFact examples and 16.538 / 1,800 / 5,400 (train / dev / test) Infotabs examples. An example of one of these tasks is shown Figure
and 2,201 278 277
Figure 11
Table-to-Text (ToTTo) In this task, the model needs to generate description based on the visually highlighted cells in given table. Instructions were generated based examples from ToTTo. Not all examples from the original dataset were retrieved, as we could not trace all highlighted cells from the dataset to the retrieved table for 8.1% of the examples. These examples were discarded to avoid adding noise to the dataset. Our dataset contains 110,934/7,077/7 ,084 (train/dev/test) examples. An
example of this task is shown in Figure 13
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Hybrid QA (HybridQA) This multi-hop question-answerii task requires integrating structured table data with unstructured hyperlinked passages. Given a Wikipedia table and texts linked to the table's entities, the model must answer multi-hop questions by reasoning across both modal- ities. Instructions were generated using HybridQA's examples, resulting in a dataset containing (2,670/3,466/3,44 (train/devhest) examples. An example of this task is shown in Figure T5
Table QA (WikiTTallequesionn Given Wikipedia table and question, the model must answer based on the table's content. For this task, WikiTableQA's examples are phrased as instructions in dataset. Our dataset includes 14.152/3.537/4.344 (train/dev/estt instructions. An example of this task is shown in Figure 17
Wikipedia Biography Generation (WikiBio) Given a Wikipedia infobox of an entity, the model prompted generate concise biography of this entity using the information in the infobox. This task's 's examples are aggregated from WikiBio. Our dataset includes 582.659972..31172..881 (train/dev/test) instructions. An example of this task is shown in Figure 14
Visual Table Question Answering Given Wikipedia table and a question, the model must an- swer based on the table's content. Questions this task require models to exploit visual features beyond textual content integrating visual perception with table understanding. This task's s examples are original to this work and were produced by human annotators (See 3.3). Our dataset includes 0/0/306 (train/dev/test) instructions. An example of this task is shown in Figure 19
MMTab's Structure Understanding tasks TABLET includes all tasks from MMTab that are meant instill table structure understanding the model. These include: Merged Cell Detection, Row & Column Extraction, Table Cell Extraction, Table Cell Location, Table Recognition, Table Size Detection, and instruction following pre- tasks. We refer to Zheng et al. (202bb)ss 's work for description of these tasks. Instructions for these tasks are directly aggregated from MMTab and tables come from the following seed datasets: Tnbs, NSF, StatCan, TabMWP, TAT-QA, TabFact, ToTTo, WikiBIO, WikiTablleQesstins.. TABLET maintains the same instructions in MMTab but uses our visualizations for the table images. As instructions originate from MMTab, and no dev set is provided in this dataset, TABLET does not include any example for these tasks in the devel- opment set. Our dataset includes the following examples: Merged Cell Detection (7,500/0/950), Row & Column Extraction (7.721/0/957), Table Cell Extraction (7,727/0/966), Table Cell Location (7, (7,708/0/956), Table Recognition (6, (6.927/0/843) Table Size Detection (7 7,800/0/950). and instruction following pre-traie pre-training tasks (136,944/0/0). example of one of these tasks is shown in Figure 18
7.6 MMTAB VS TABLET
Table provides detailed comparison between MMTab and TABLET across tasks and example counts (in training, development and test sets).
SUPERVISED FINE-TUNINE DETAILS
All supervised fine-tuning (SFT) experiments were carried out with the same hyperparameters across models; the only varying factor was the dataset used for training. We fine-tuned Swwen255-VL-7B. Instruct using their official implementation Our code will be released alongside the dataset in their project repository.
Hyperparameters
All runs used the following common configuration:
Training setup: DeepSpeed ZeRO-3, bf16 precision
Epochs: 3
Batch size: 2 (per device), gradient accumulation steps: 4 (for 32 GPUs) 8 (for 16 GPUs)
Optimizer: AdamW, learning rate: 2e-7, weight decay: 0.01
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Train
Dev
Test
Task
MMTab
TABLET
MMTab
TABLET
MMTab
TABLET
wikibio
4,994
582,659
72,831
1,000
72,831
wikitq
17,689
14,152
3,537
4.344
4,344
totto
15,000
110,934
7,077
7,700
7,084
tabmwp
30,745
23,059
7,686
7,686
7,686
tabfact
31,321
87,717
12,389
6,845
12,326
hitab
11,941
7,417
1,670
3,160
1,584
infotabs
18,338
16,538
1,800
5,400
5,400
fetaqa tat-qa
8,327
3,006
577
2,003
1,079
5,920
2,201
278
772
277
table instruccitnnn
37,204
136,944
0
0
table.celleextraction
8,000
7,727
0
1,000
966
lableton
8,000
7,708
0
1,000
956
table_size.detection
8,000
7,800
0
1,000
950
merged__cell_detecioo
8,000
7,500
0
1,000
950
row www.column.com extraction
eoturnnextractioon
8,000
7,721
1,000
957
recoonnccnnn
8,000
6,927
1,000
912
rotowire
3,400
334
0
cop.type
602,406
13,188
12,802
ent_link
0
1,236,122 128
74,282
213,494
rel extraction
0
60,615
2,145
2,030
hybridqa
0
62,670
3,466
3,463
struct aware aware.parse
513,482 0
9,115
1,102
OOD
0
0
1,250
0
TabMCQ
0
0
1,029
0
AIT-QA
0
0
511
0
Tub
0
1,942
VisualTableQA
306
All
232,879
3,505,311
210,041
49,976
351,499
Table 7: Comparison of MMTab and TABLET: tasks and examples across training, development, and test splits.
Scheduler: cosine decay with warmup ratio 0.03
Gradient clipping: 1.0
Sequence length: 8192 tokens
Vision input size: max-pixels 50,176, min.pixels 784
Other: data flatten = False, data packing = False, tune momeemm.iie vision Fa 1 se, ture mm mlp True, tune mm 11m True
Compute Usage reports the total GPU hours consumed by each experiment. All runs were conducted on clusters equipped with NVIDIA A100 GPUs.
8.1 INSTRUCTION SEQUENCE LENGTHI
Figure shows how instruction length (measured in terms of tokens) varies across tasks in TABLET. As can be seen, HybridQA has the longest instructions, followed by Column Type, Entity Linking, and Relation Extraction.
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Dataset
Sctup
GPU Hours
TABLET-Borg
15h x 16 GPUs
240
TABLET-BA synth
15h x GPUs
240
TABLET- mix
21h x GPUs
336
MMTab
21h x 16 GPUs
336
TABLET-S
28h x 32 GPUs
896
TABLET-M
40h x 32 GPUs
1280
TABLET-L
125h x 32 GPUs
4000
Inference (TABLET-a oorg)
21h models
168
Inference (TABLET synth)
21h X models
168
Inference (TABLET-BE mix)
24h x models
192
Inference (MMTab)
24h X models
192
Inference (TABLET-BL)
34h x models
272
Table GPU hours for supervised fine-tuning and prediction runs. For the predictions, the models the 7 SFT models and the baseline model.
Mean Token Length by Task with Standard Deviation
10*
Mean Token Length Hlog scale)
10a
ent Josk
extrantion
Intatla
Eabelacz
labrwp
intooha
bntto
hybridsa
wnkid
bitle aore detertion
table rell Jocaten
now coloee, extraction
reergedd cell detection
tabie ret extraction
baile recopiieon
Babies insruction
REueE awere
Task
Figure Instruction length distribution across tasks in TABLET.
8.2 PERFORMANCE DEGRADATION: ORIGINAL SYNTHETIC IMAGES
Let each task belong to one of the metric families B (BLEU), A (accuracy), or F (F1), For given model m, we compute the raw difference:
A m.t Score org mi. Score synch m.!
Negative values indicate that the model performs worse when evaluated on the original visualizations compared to the synthetic ones. Because the metrics differ in scale, we normalize each by the corresponding synthetic score to report results in terms percentage change:
Am,t
6m,t
synth X 100.
Score m.t
Next, for each metric family F E A, we compute the family mean (M). Finally, the Degrada- tion Score for model m defined as the unweighted average of the family means:
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1 { MM.. + MM,A + Mm..).
DegScore
3
This guarantees that BLEU, accuracy, and Fl contribute equally, regardless how many tasks are included in each family.
8.3 OPEN-WEIGHT MODEL EVALUATION ON TABLET
Finally, in Tables 9 and 10 we report results on TABLET for open-weight VLMs other than Qwen2.5-VL-7B, in the zero-shot setting.
WikiBio
ToToo
WikiTQ
TabbWW
Hilab
TabFFac
FeTaQA
TAT-QA
Table-LLaVA 7B Zheng
2024b
5.7
16.3
17.7
47.5
10.6
53.6
1.8
Qwen2.5-VL 7B Qwen ct al.
2025
2.5
9.1
53.4
59.1
31.2
73.9
7.0
6.9
DocOwi2 8B Hu et 2025
1.8
1.2
18.9
0.5
9.5
54.3
9.6
InterVL33 8B (Zuu et al.
2025
4.6
11.9
45.3
83.9
40.8
64.3
3.8
13.0
IntemVL3 14B
(hu et 2025
6.3
12.4
51.6
86.8
52.0
75.1
6.6
11.9
Gemma-3 12B
Team et al.
2025
5.1
5.4
39.1
72.3
24.2
69.5
20.3
2.5
Table Open-weight VLMs evaluated on TABLET held-in datasets (zero-shot setting).
Infotabs
TabMCQ
AITQQA
Publlealhhaab
HlybriQQA
TabRec
VTQA
Table-LLaVA Zheng et al. 2024b
4.7
50.2
Qwen2.5-VI. VL. 7B (Owen et al. 2025
64.5
84.4
$1.7
64.7
34.2
24.5
42.4
DocOw12 8B Hu al.
2025
15.5
63.0
36.8
14.6
4.6
9.5
InterVLL3 iZhu ct al.
59.6
52.4
64.2
33.1
13.6
43.0
IntemVL3 14B (Zuu a
2025
67.4
88.8
52.3
74,9
46.7
13.4
49.9
Gemma-3 Team al.
64.7
35.2
28.5
Table 10: Open-weight VLMs evaluated on TABLET held-out datasets (zero-shot setting). Results for HybridQA not reported for Table-LLaVA 7B and DocOw12 8B due to their VRAM require- ments, which do not scale well to the long contexts needed for our dataset.
LIMITATIONS
While TABLET is the largest resource for Visual Table Understanding to date, it has several lim- itations. Firstly, some original visualizations could not be retrieved due to missing or changed Wikipedia pages, and some embedded resources (e.g., images) were inaccessible. Secondly, not conduct a full ablation of the contribution each task due to computational constraints, focus- ing instead broader questions such task balancing and dataset inclusion. Thirdly, most seed datasets are from Wikipedia, which, despite differing in visual format, are common in pretraining corpora, raising the possibility of data contamination. Finally, fine-tuning focused model (Qwen2.5-v6 L- 7B), with zero-shot evaluation on others; future work should explore a broader range of architectures, especially those with reasoning self-reflection capabilities.
did
single
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Table:
Place
Athlete
Time
Qual.
1
Mel Brock (CAN)
1:57.0
2
Ted Meredith (USA)
QS
3
John Victor (RSA)
25
Alan
DNF
Patterson (GBR)
Instruction:
For
the Wikipedia table from the article 'Athletics at the 1912 Summer 0lympico cs Men 800 metres' see 'Heats', select the proper entity that matches the highlighted table value given the following possible entities (<name description / type>): <Alan
Patterson Mikipedia
disambiguation page / None>, <Alan Patterson British athlete / owl#Thing>, <Alan Patterson / UK MF / Person>, I...l. Return only the identifier or name of the chosen entity as JSON: ("answer"; "YOUR ANSWER").
Expected output:
("answer": "<Alan Patterson / British athlete / owi+Thing>")
Figure 6: Example for Entity Linking task based on highlighted table cell.
Table:
Year
Competition
Venue
Position
Event
Notes
Representing
Soviet Union
1976
Olympic Games
Montreal, Canada
3rd
x 100 relay
European Championships
Milan, Italy
Instruction:
According to the table titled Nikolay Kolesnikov
(sprinter), section Achievements, write short the question: How did Nikolay Kelesnikov do at the 1976 Olympies and at the 60
sentence answer
metres at the 1978 Karopean Indoor Championships?
Output only the correct answer as ["answer": "YOUR ANSWER").
Expected output:
"answer"; "Nikolay Kolesnikov won bronze medal at the 1976 clyppics and won the 60 metres at the 1978 European Indoor Championships.
Figure 7: Example for Free-form Table Question Answering task based highlighted table cells (FeTaQQA).
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Table:
Nationality
Player
Ranking*
Seeding
ISR
Dudi Sela
56
1
TPE
Yen-hsun
2
USA
Bobby Reynolds
93
3
GER
Michael Berrer
111
4
BRA
Thiago Alves
120
5
GER
Benjamin Becker
126
6
FRA
Nicolas Mahut
137
7
RUS
Michail Elgin
138
8
Instruction:
For the table found the article '2009 isracl open' (section 'seeds") Wikipedia, determing which relation holds between the two highlighted columns among these relation cancicates:
base.
award
[...].
Previce the correct relation as JSCN: (*answer"; ["RELATION"]] list multiple
relations comma separated if necessary.
Expected output:
{
("answer"; ("people, preople.person www.pp.r..nne..o. nationality"))
Figure 8: Example for Relation Extraction task based on highlighted table columns.
Table:
Practice summary
Fastest lap
Session
Day
No.
Driver
Team
Car
Time
Practice
Friday
David Reynolds
Erebus Motorsport
Holden VF Commodore
1:12.8697
Friday
Whincup
Triple Eight Engineering
Instruction:
Answer
the given question concisely possible using information from the table. Answers must be
within USON object e.g. ("answer"; [*YOUR ANSWER"]] List multiple answers with commas inside the list object. Table caption: 'practice', Relying on the table, anawer the followi ng: what
was the time of david zeynolds in practice 1?
Expected output:
["answer::
Figure 9: Example for Hierarchical Table QA task (HiTabQA).
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Table:
Instruction:
Convert the illustration to Markdown syntax.
F111.
Expected output:
20:00
<md>
Diagnosis Cases
Indeterminate Evaluable
--- --- ---
Follicul 34 6/34 (17.6%) 28/34 (82.48) I
Mantle cell
0/7 (08) 1 7/7 (1008) |
Marginal Zone 11 1/11 (98)
10/11 (918)
Lymphoplasmacytic 6 0/6 (08) I 6/6 (100%)
Di fifueee large B Cel1 | 15 2/15
(13.38) I 13/15
(86.78)
CLL/SLL Small Cell NHL, NOS
0/4 (08) 4/4 (1008)
2 0/2 (08) 2/2 (1008)
Total 79 9/79 (11.38) 70/79 (88. 6%) </md>
Figure 10: Example for Structure Aware Parsing task.
Table:
Instruction:
Plants per garden
The members of the local garden club tallied the
+ number of plants in each person's garden. How many
Stem
Leaf
4 gardens have at least 47 plants?
33355
Solve the above problem according to the table called 'Flants per garden'. Include your reasoning, after which finish with the JSON ("answer"; "YOUR ANSWER").
6
4 5 7 8
7 8
Expected output:
7
2379
Find the with stem 4, Count all the leaves greater than equal to 7.
689
Count all the leaves the rows with stens 5, 6, 7, and
You counted 13 leaves, which are blue in the stem-and-leat plots above. 13 gardens have at least 47 plants. Therefore ( ("answer"; "13")
Figure 11: Example for Table Numerical Reasoning task (TabMWP).
Table:
Instruction:
Charles Francis Jenkins
Consulting the table, state whether the hypothesis is entailed, neutral, or refuted. Give your final answer JSON format ("answer"; "YOUR ANSWER").
Hypothesis: Charles Francis Jenkins has been awarded more than one medal.
Destos Ohey
Expected output:
waitinggnnn D.C.
Netionalty
Anaokar
["answer": "entailed")
nlocation
Erginearing
Projects
Over 500 putannt
variice
advance
(U007)
Jo11 Sccre Headel (3932)
Figure 12: Example for Table Fact Verification task (Infotabs).
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Table:
Report
jul
19:30
Slovakla
Netherlands
21
18
25
95-87
Report
Instruction:
Consulting the following table concerning '2008 Men's
European Volleyball League' in the 'Final'
section, give one-line statement limited to the highlighted cells and return as JSON:
("answer"; "YOUR ANSWER").
Expected output:
["answer": "The 2008 Men's European Volleybal1 League
was won by Slovakia, defeating the Netherlands by 3(1 in the finals.")
Figure 13: Example for Table-to-Text task based on highlighted table cells (ToTTo).
Table:
Instruction:
Drawing upon the provided infobox of piet van zyl
wkish move
-irb- namibian rugby union player -rrb-, write a third-person, encyclopedic biography and format within the following JSON object ["answer"; "YOUR OGRAPHY").
Dutw teath May 19m sepas
Place sork Wereedee
Height
Whighh
Baddayy anREe
Seceer carner
Expected output:
Years
Team
2089-12
Dosight
1471
["answer"; "2iet van zyl (born 14 may 1979) a
Cusertt
patata
namibian rughy union player who captained the
Tears
1eem
boland cavaliers in south africa
provincial level, and played for the at international level.
2E:5-000
Van zyl was in the namibian squad for the 2007 world cup, and scored try in his nation 's first
match in the competition, in a game against. Van
zyl plays as a centre. Van zyl made his debut in august 2007 in a friendly match against.")
Figure 14: Example for Wikipedia Biography Generation task (WikiBio).
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Table:
Rank
Gymnasts
Country
Point
Allila Katus. Tamas Romeo Szentgyorgy
Hungery
18.35
Donel Mais, Moldovan, Claudiu Varfam
Romania
Maria Halm cron, Helane Nilsson, Kim Wickman
Sweden
15.87
Merchorkav, Michailov,
Grégory Alcan. Xavier julien. Olivier Salven
France
15.00
Won-Sil Hyun-Sung <wang Soo Park
South
Maric Catherine Bocss, Jana Heinzo, Sancha Schlucteor
Gormany
14.835
Yumi Kobeyashi, Kumi Hiroko Watabe
13.750
Glocoroo Pkccll, Giovanna Lecis. Marco Bisciulo
Rolly
13.044
Instruction:
Taking into acccunt the accompanying table as well as the context snippets provided at the end.
Answer the following question: What is the official
name of the country that finished with the fifth most points at the 1998 Aerobic Gymnastics World Championships Respond with the correct answer (omit explanations) in JSON format as l"answer"; "YOJR ANSWER"). Do not introduce information beyond the provided sources.
Hungary is country in Dorel Moi is a retired [.::;]
Claudiu Cristian Moldovan is retired Romanian aeropic gymnast I...]
Claudiu Varlam is a retired Romanian aerobic [...] Romania is country located [...]
Sweden officially the kingdom of Sweden is I...] Russia the Russian Federation is [...] France officially the French Repablic
is [...]
South Korea officially the Republic of Korea is l...]
Germany constitutionslly the Federal Republic - Germany
[...]
Japan is island country located in l...] Italy officially the Italian Repablic is [...]
Expected output:
["answer"; "French Republic")
Figure 15: Example for Hybrid QA task (HybridQA).
Table:
Instruction:
Based the table image, extract the value the cell located in the subsequent postion:
Misidter fouture Executiive Yuans
the 1st row and the 1st column
office Febvaary 2012
Format the cell value as a JSON, using the structure ["row_id"; "m", "colunn_ "d";""" "cell_value"; "cCorresponding Cell Value>").
Preceded Cuche cloues
Rascomaeed than
Hisister coundi
Ag agriouture Executive Himm
effice
Expected output:
Masster
Ciai Hommar
Paruonal derialls
The target cell value the 1st row and the 1st
Dom
11 March 1044 Iege
column is ("row id"; "column "column_id":"1" "cell_value"; "Chen wu-hsiung").
nater
liiing U Urtonaa-rampaatnn
Figure 16: Example for Table Cell Extraction.
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Table:
Busiest international routes at Manzanillo International
Airport (2013)
Rank
City
Passengers
Ranking
Airline
United States,
Alaska
1
14,749
Los Angeles
Airlines
United States,
United
5,465
Houston
Express
I+ Canada,
Transat, Westjet
3,761
Calgary
I+] Canada, Saskatoon
2,282
I+ Canada,
2,103
Transat
Vancouver
United States,
1,829
A1
Airways
Phoenix
I+ Canada,
Air Transat, Canjet
1,202
Toronto
I+ Canada,
8
110
Edmonton
United States,
9
107
Oakland
Instruction:
Answer this: How many more passengers flew to los angeles than to saskatoon from manzanillo airport 2013? Consult the table and answer. Return your answer USON: {"answer"; "YOUR ANSWER") Avoid including information not present in the table.
Expected output:
"answer": "12, 467")
Figure 17: Example for Table QA task (WikiiTableQuestionn).
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Table:
Nationality
Player
Ranking*
Seeding
ISR
Dudi Sela
56
1
TPE
Lu Yen-hsun
2
USA
Bobby Reynolds
93
3
GER
Michael Berrer
111
4
BRA
Thiago Alves
120
5
GER
Benjamin Becker
126
6
FRA
Nicolas Mahut
137
7
RUS
Michail Elgin
138
8
Instruction:
For the table found '2009 israel open:
4
"seeds" section on Wikipedia, column type Labels for the hignlighted column given the following type options: ww.tw_porsonality, time. event, l.
identiff the
I...]. Provide only the chosen (s), separated by commas if multiple, within the list in this JSON: !"answer"; ["ANSWER"]].
Expected output:
("answer:: ("location conaiion.oo octtion.oountry" "iocation. location"!
Figure 18: Example for Colum Type Annotation task based on highlighted table columns.
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Table:
Team
Points
UST Tigresses
FEU Lady Tamaraws
21.5
UP Lady Maroons
21.0
Ateneo Lady Eagles
13.5
UE Amazons
8.0
NU Lady Bulldogs
9.5
De La Salle Lady Archers
Suspended
Instruction:
Here is the question: Among the teams
featuring blue in their colors, which
one get the highest points? Using only the table, provide your answer. Return
your answer as JSON: ("answer"; "YOUR
ANSWER"); Avoid including information
not present the table.
Expected output:
["answer": "Ateneo Lady Eagles")
Figure 19; Example for Table Visual Question Answering task.
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10 TABLE VISUAL COMPLEXITY METRIC
We create a visual complexity metric that assigns each table a score E [0, 1], where denotes visually simple, regular, monochromatic table and I denotes highly visually complex table. In practice, most complex tables in TABLET reach a score of 0.54 (see the distribution scores in Appendix IT]. The metric aggregates multiple structural and visual table characteristics: spanning irregulartty, color and font diversity, image presence, entropy, and more, into a single normalized value via weighted sum (weights listed in TT).e The score combines structural HTML features with visual image analysis metrics.
10.1
HTML STRUCTURE METRICS
Let N denote the total number of cells in the table. We extract the following structural features:
Span Irregularity Sspan
Regular tables have cells occupying exactly one column and one row (colspan= 1, rowspan= 1).
We measure irregularity through two components:
(1) Proportion of irregular cells: Let col and n denote the number of cells with colspan=
1 and rowspan= 1, respectively. The proportion irregularity is:
(1) n col
(I)
col prop
now
nlow
N
prop
N
Span magnitude: Let 6; and denote the colspan and rowspan values for cell i. The weighted span magnitude is:
N
N
pool mag
FLwW mmug
T
(2)
N
max 1)"
N
max (Ti,
1=1
The final span irregularity combines both components with equal weight:
1 1
Sapan
+ - (Ppppp
I mag
(3)
2
2
Color Diversity Scolor
Let The and Tlxrt denote the number of unique background and text colors. Simple tables typically use 1-2 colors. We compute:
S
min
flg
.1 + min
Tlext - 1 .11 :]
(4)
2
5
5
Font Diversity Stont
Let Tioos denote the number of distinct font families. Simple tables use single font:
Tooont
S min
(5)
3
Image Presence Sing
Let Rimng denote the number of embedded images. We normalize by table size:
Nang
Sing
min
(6)
0.3 N
10.2 VISUAL IMAGE METRICS
Given a rendered table image I E Hxx33 we compute the following visual features:
Visual Entropy Sentropy
We convert the image to grayscale and compute the Shannon entropy of the intensity histogram. Let p(i) denote the normalized frequency of intensity value {O...., 255}:
E25 p(i) log p(i)
255
Sentropy =
log 256
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High entropy indicates pixel intensity randomness, while low entropy suggests uniform regions.
Color Complexity Septx
Let Ranique denote the number of unique RGB triplets. We normalize by fraction of total pixels
P x W
Muniqqu
Sepx min
(.1 P
Edge Irregularity Sedge We apply Sobel operators to detect horizontal (Vx) and vertical (Vy) edges. Regular grids exhibit balanced edge distributions:
af
Incan })
Sedge min
imcan
50
where a denotes standard deviation and
the mean absolute gradient magnitude.
mean
Color Saturation Sat Converting to HSV color space, let Sisv denote the saturation channel. The saturation score is:
S
255 P Σ
ESHsv
Susv (h, w)
(10)
h,30
Non- White Background Ratio Sing Let Putte denote pixels where all RGB channels exceed 240. The non-white ratio is:
1P IPhiite
Sbg
(11)
10.3 FINAL COMPLEXITY SCORE
The overall complexity score is weighted combination of all component scores:
S
Sa
(12)
where the weights we sum 1. We the following default weights:
Component
Weight (wx).
Span Irregularity (Sypa)
0.12
Color Diversity (Seoke)
0.15
Font Diversity (Spoa
0.08
Image Presence (Sing)
0.10
Visual Entropy (Sentroyy)
0.15
Color Complexity (Segns)
0.12
Edge Irregularity (Sedge)
0.10
Color Saturation (Stm)
0.10
Non-WWite Background (Sag)
0.08
Table 11: Component weights for table complexity score computation.
The resulting score S E 00, 1] provides a continuous measure of table complexity. Figure 20 shows four tables with progressively increasing visual-complexity scores.
11 TABLE VISUAL COMPLEXITY ANALYSIS
We report the full visual complexity analysis across all benchmarks in Figure 21 For each task, report model performance as a function of table visual complexity using the metric introduced in AppendixTO
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Mlemcark
Scare
2003
But dogg
Cortars Fieily Beach
14.11 361
18.3
2004
17.1
Cunc juto
153
Croo Doyer teee
166
1113
2004
11.11 (71)
ldved
108
Centra Tiwiyy Seuch
14.16 2168
118
2008
Carre Sertss
12.15 391
2003
undo mevks
11.8 176
2002
Calre Santa
S = 0.04192
= 0.17608
= 0.26386
= 0.39221
Figure 20: Example tables with increasing visual-complexity levels and their corresponding scores.
0-Shot
MMTab
TABLET-S
TABLET-M
TABLET-L
ToTD>
FrTeee
HIT
TAMAR
30
31
Compinity
Corplaity
HylaiiQA
TATTQA
(0
(0
10 12
22
2.4
288
3.5
Voul Cooplestt
Yould Complestt
Voua Coaplestt
Viuua Cooplestt
WELBii
Mia VC 855
10
20C
16L
100 1000
12
30
12
15
Visaal
Viaad Completty
MuaaCCompedii
Figure 21: Model (Owee2.5-VL--1-Insttu) performance across levels of table visual complexity (x axis). Higher levels correspond to more visually complex tables. Complexity levels are compara- ble across tasks, that is, e.g., level represents the same complexity range every benchmark.
12 GEMMA 3 TRAINING SETUP
We fine-tuned the Google Gemma-3-4B-IT model using Low-Rank Adaptation (LoRA) on TABLET- M. In this section disclose the training syper-parameters.
Base Model:
3-4b-it
Quantization: 4-bit NormalFloat (NF4) with double quantization
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Compute dtype: bflcat16
Attention Implementation: Flash Attention 2
Maximum Sequence Length: 8,192 tokens
LoRA Rank (r): 16
LoRA Alpha (o)): 16
LoRA Dropout: 0.05
Target Modules: All linear layers
Bias: None
Additional Trainable Modules: 1m Imade embed.tokert tokens
Number of Epochs:
Per-Device Batch Size:
Gradient Accumulation Steps: 64
Effective Batch Size: 256
Learning Rate: 2 x 10-4
Learning Rate Scheduler: Constant
Optimizer: AdamW (fused implementation)
Warmup Ratio: 0.03
Maximum Gradient Norm: 0.3
Precision: 6 mixed precision training
13 VISUALTABLEQA QUESTION CATEGORIES.
VisualTableQA examples can belong to one or many of the following categories:
V1. Image-based Attribute Identification: Requires interpreting pixel-level image content
within table cells (e.g., Example 22a
V2. Visual Formatting-based Selection: Relies on non non-image visual cues such as text color, cell
shading, or row styling (c.g., Example (2b).
S1. Structural / Positional Reasoning: Involves reasoning over table layout or relative position
(e.g.. Example
R1. Aggregation and Numerical Reasoning: Requires counting, aggregation, or identifying
extrema (e.g., Example
R2a. Single-hop Conditional Retrieval: Retrieves value from row identified by single
condition (c.g., Example 22c).
R2b. Multi-hop/ Comparative Retrieval: Requires multiple constraints, comparisons, nested
selection (e.g., Example
B1. Multiple Choice: Questions formulated as binary multiple-choice (e.g., Example 23a
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(a) V1. Image- bused Attribute Identification
(b)
V2. Visual Formatting--unn bussd Selection
Place
Team
Prize money
jamves
1st
Digital Chaos
US$125,000
2nd
Newbee
US$50,000
M/4
2113 950
Wings Gaming
US$22,500
3rd
Press
Virtus, pro
US$22,500
I+
Team NP
US$10,000
5th
Execration
US$10,000
1819
Candl
Fnatic
US$5,000
7th
2110
WarriorsGaming.no Unity
US$5,000
Q: Which team is in the gray row?
18%6
1144
Newbee
Rishordd
Q: Is Edhmund J. Davis wearing a militury uniform?
yess
(c) S1. Structural/ Positional Reasoning
(d) RI. Aggregation and Numerical Reasoning
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2012
107
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2012
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Q: How many different nationalities are shown in the table?
A:
Q: Which team is directly above Australia the table?
A: Englond
(c) R2a. Single-hop Conditional Retrieval
(f) R2h. Multi-hop / Comparative Retrieval
Sesson
Level
Division
Section
Position
Movements
City Population Massquults
Iawarn
Du Rgsggs
1405
Ter
Division
Nariand
3rd
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Promotes
1359
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10th
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Macsdens 2008
2201
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2nd
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2002
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Promotes
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2005
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2009
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Which country red house belong to!
A: Budgaria
Q: Which the movements for the entry that has the same background color as 2004 and 2005?
A: Relegated
Figure 22: Example questions from each VisualTableQA category (part of 2).
36

---

Published as a conference paper at ICLR 2026
BL. Multiple Choice
Connas
Testint
Cansowarr
15.000
Canrnwryy
10ml
trons
Q: What the trend of the bird with the lowest population? Answer: positive, negative, same, unknown.
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Figure 23: Example questions from each VisualTableQA category (part 2 of 2).
37


Reviews:
[{"review_id": "tn5fpDN6Vr", "reviewer": "Reviewer_czgq", "summary": "This paper presents TABLET, a large-scale dataset for UTV, containing 4M examples. The key contribution is the collected TABLET from sources from Wikipedia and PubTabNet. The dataset are split into train, dev, and test sets and comprising comprehensive table-related tasks. Experiments with Qwen2.5-VL-7B show good performance gains on several benchmarks, and the authors argue that TABLET improves robustness on real-world table images.", "questions": "Please refer to the weaknesses section above. \nIt seems that the author merely proposed a dataset and fine-tuned qwen with it, without providing comparisons to other models. I believe the paper still needs further polishing.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "+It collects a large-scale dataset combining multiple VTU sources. \n+It provides both HTML and image representations with metadata, which facilitate the document understanding community. \n+Experiments on the Qwen2.5-VL models demonstrating the effectiveness of the collected dataset.", "weaknesses": "1. The author presents a new dataset, TABLET, and the author claims that the dataset contains 4M examples. However, nearly 30% are existing, the author should describe this more rigorously.\n2. No new model, algorithm, or theoretical insight in the paper. The paper mainly merges existing datasets and collected new table data on the network. \n3. One of the author’s main motivation is that synthetic data are of lower quality than real-world data. However, the majority of the dataset they collected still consists of screenshot-based images, many of which are taken from sources like Wikipedia. This seems somewhat inconsistent with the author’s stated motivation?\n4. Poor experiments. In Tab.2 and 3, the performance improvement by the proposed dataset appears to be limited.\n5. What confuses me is that the paper only reports results for qwen and the results fine-tuned on qwen, without any comparison to other models. This is completely unreasonable for a research paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Rq2g3Ol0Gb", "reviewer": "Reviewer_P6aP", "summary": "The paper introduces TABLET, a large-scale dataset for visual table understanding (VTU). Compared to existing resources (e.g., MMTab), TABLET (i) reconstructs tables in their realistic visual form from web/Wikipedia sources, (ii) unifies them into an image + HTML + metadata format, and (iii) covers 20 VTU-related tasks. The authors fine-tune Qwen2.5-VL-7B on this data and show improvements on both seen and unseen VTU tasks. The main value of the paper is on the data/engineering side and on providing a more realistic training distribution.", "questions": "None", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.The motivation is real: current VTU data is mostly synthetic or visually simplified, which causes a distribution gap for vision-language models that must operate on real web-like tables.\n\n2.The dataset is large, diverse, and traceable back to the original sources, which is helpful for reproducibility and later task extension.\n\n3.Experiments are fairly comprehensive (synthetic vs. real-style vs. mixed) and the results are consistent with the stated motivation.", "weaknesses": "1. The paper does not introduce a new learning objective, a new model architecture, or a principled framework for unifying VTU tasks. The core message is essentially “better/realistic data → better performance,” which is valuable but closer to a dataset/empirical paper than to a method/theory paper.\n\n2. The paper repeatedly argues that preserving realistic visual styles leads to better generalization, but the current evaluations mostly show dataset-level gains, not an analysis of which visual factors (borders, multi-row headers, fonts, layout noise) the model actually exploits.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "sIcCuUSe7T", "reviewer": "Reviewer_WHyK", "summary": "This paper identifies a critical \"train-test mismatch\" in the field of Visual Table Understanding (VTU). The authors argue that existing benchmarks predominantly use \"synthetic renderings\" of tables, which lack the visual complexity (e.g., merged cells, colors, fonts, embedded images) of real-world tables. This causes models trained on them to fail when generalizing to real-world visual data.\n\nTo solve this, the authors introduce TABLET, a new large-scale dataset of 4 million examples over 20 tasks. The dataset's primary contribution is that 88% of its 2 million unique tables are \"original visualizations\" meticulously retrieved from historical web snapshots (primarily Wikipedia), preserving their true visual fidelity. The authors demonstrate through experiments that while performance on traditional tasks is mixed, training on TABLET dramatically improves a model's robustness to visual domain shift (synthetic vs. original) and enhances generalization to unseen tasks.", "questions": "1. Given that the core experiment in Table 2 fails to show a clear superiority for TABLET-B_org over TABLET-B_synth, and you attribute this to flawed benchmarks (e.g., ToTTo), does this not imply that the primary contribution of this work is a resource for which the tasks do not yet exist?\n\n2. The strong performance of the TABLET-B_mix model seems to suggest that a combination of synthetic and original data is optimal. Does this not contradict the paper's central premise that synthetic renderings are inherently flawed and should be replaced by original visualizations?\n\n3. Why were new, visually-demanding tasks (e.g., \"find the cell with the red background,\" \"what is the value in the merged cell?\") not introduced and evaluated to actually prove the hypothesis that the visual fidelity of TABLET is its key advantage?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. **Problem Identification:** The paper's core premise is strong and well-articulated. It correctly identifies the \"train-test mismatch\" as a fundamental problem for the VTU field as it shifts towards pixel-only VLMs. This is a significant and practical issue.\n\n2. **Methodological Rigor on Data Collection:** The primary contribution is the dataset's \"visual fidelity.\" The engineering effort to achieve this is non-trivial and highly commendable. The authors detail a rigorous process of tracing seed datasets to their original crawl dates and using Wikipedia's archiving API to retrieve historical snapshots, followed by Levenshtein matching to find the correct table. This high-quality execution is a major strength.\n\n3. **Strong Robustness Argument:** The paper's most compelling evidence is in Table 9. It clearly shows that models trained on synthetic data suffer a massive performance degradation (-22.35 points) when evaluated on original visualizations. In contrast, models trained on TABLET's original data are far more robust (only -6.63 points). This single finding strongly validates the paper's core hypothesis and the necessity of this dataset.\n\n4. **Resource Value and Extensibility:** The paper delivers more than just a static benchmark; it provides a large-scale, multi-task resource. By including HTML representations, metadata, and traceability links to source datasets, the authors enable future research, task reformulation, and extensibility.", "weaknesses": "1. **Core Claim Undermined by Own Results:** The paper's most significant weakness is that its primary experiment (Table 2) fails to support its central claim. When comparing models trained on TABLET-B_org (original) vs. TABLET-B_synth (synthetic), the performance is roughly equivalent across most tasks. This result is underwhelming and directly contradicts the motivation that visual fidelity improves performance on these tasks.\n\n2. **Circular Argument:** The authors' explanation for the failure of Table 2 is that the benchmarks themselves (e.g., ToTTo, WikiTQ) are flawed and were designed for text, not visual cues. This is a \"catch-22\" and a form of circular reasoning: the paper claims its dataset is the solution but simultaneously admits that the tasks it uses for validation are incapable of proving it. They have built a high-fidelity testbed but failed to provide tasks that actually require that fidelity.\n\n3. **Diluted Conclusion on \"Mixed\" Data:** In several experiments (Table 2, Table 10), the TABLET-B_mix (original + synthetic) model performs best. This dilutes the paper's central premise. The takeaway risks becoming \"more data is better\" (a trivial conclusion) rather than the intended, stronger claim that \"high-fidelity data is better.\"\n\n4. **Limited \"Real-World\" Diversity:** The paper claims to solve the \"real-world\" visualization problem, but the dataset's diversity is questionable. 88% of the data (61.5% of tables) is sourced from Wikipedia. While better than synthetic data, Wikipedia tables are still highly structured and relatively uniform. This is not representative of the true \"in-the-wild\" chaos of scanned PDFs, financial reports, or product pages.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "34eEiHvLuG", "reviewer": "Reviewer_qfkf", "summary": "Current visual table understanding (VTU) benchmarks rely on synthetic data lacking real-world complexity and omit underlying serialized data, while TABLET is a large-scale dataset with 4 million examples from real visualizations, paired image-HTML representations, and 20 tasks. Fine-tuning vision-language models on TABLET boosts their performance on seen/unseen VTU tasks and robustness to real-world tables, establishing a foundation for future VTU model training and evaluation.", "questions": "see the weakness", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "TABLET addresses prior single-task/synthetic data limitations with large-scale, lossless real-world table visualizations and 20 diverse tasks, while its high quality lies in 4M examples, image-HTML pairings, and traceable metadata. Its clear unified format and extensible design, coupled with significant contributions to boosting VLM cross-task performance (including SOTA results) and enabling practical VTU research, underscore its field value.", "weaknesses": "1. Whether all tables are presented in English? As far as I am aware, Wikipedia provides original HTML files in multiple languages.\n2. Each example in TABLET is accompanied by the HTML version of the corresponding table. Can such a large volume of data be used to train table parsing models? Is the annotation format of HTML consistent? For instance, how are formulas within the tables annotated? Additionally, do these HTML files contain certain formatting information, such as row widths and font colors?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)