---
title: "MMTabReal — paper by Titiya et al. (2026) — summary and ideas"
description: "Multimodal large language models (MLLMs) still struggle with tables that mix text and visual content such as logos… 23.9 points. 47 references."
authors: ["Prasham Yatinkumar Titiya", "Jainil Trivedi", "Chitta Baral", "Vivek Gupta"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.2047/"
doi: "10.18653/v1/2026.findings-acl.2047"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/titiya-2026-mmtabreal-real-world-benchmark-multimodal/"
---
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# MMTabReal: Real-World Benchmark for Multimodal Table Understanding

**Authors:** Prasham Yatinkumar Titiya, Jainil Trivedi, [Chitta Baral](https://md.research.elamur.ai/free/en/authors/chitta-baral/) and [Vivek Gupta](https://md.research.elamur.ai/free/en/authors/vivek-gupta/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.2047/) · [PDF](https://aclanthology.org/2026.findings-acl.2047.pdf) · [DOI 10.18653/v1/2026.findings-acl.2047](https://doi.org/10.18653/v1/2026.findings-acl.2047) · [OpenAlex](https://openalex.org/W4416046366)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Multimodal large language models (MLLMs) still struggle with tables that mix text and visual content such as logos, icons, color coding, maps, and small embedded charts. Earlier benchmarks like MMTabQA and MMTab mostly used synthetic images or simple visual elements. To fill this gap, the authors built MMTabReal, an evaluation-only benchmark of real tables from open-license sources. Scanned tables were reconstructed by hand, and NLP experts wrote the questions. Every question depends on at least one image, either directly or as an intermediate reasoning step. Questions are labeled by type (explicit, implicit, answer-mention, visual-based) and by the reasoning they require. Quality was checked through filtering, internal double review, and external validation. When state-of-the-art models are tested, exact match accuracy falls between 21.6% and 48.1%. Scores are close to zero on image-location and spatial tasks, and performance is clearly lower than on MMTabQA. The benchmark exposes weaknesses in visual grounding, layout alignment, and multi-step inference. It points toward architectures that integrate vision more tightly with table structure.

## Key points
- The benchmark contains 500 human-curated tables and 4,021 QA pairs. These cover 4 question types, 5 reasoning categories (extrema, math, fact verification, vision-based, others), and 8 structural archetypes. Each table averages about 23.7 images, and roughly 89% of rows include images.
- Annotation agreement was strong. Internal review reached an overall Cohen's κ of 0.82 and corrected about 11% of answers. Three external annotators checked 804 questions (a 20% subset) against the gold answers and reached κ = 0.85.
- Compared with MMTabQA, substring-match accuracy drops by 7.6 to 23.9 points across baseline settings. The settings are missing image, entity replaced, image captioning, table as image, and interleaved. The largest drop appears in the missing-image setting.
- Gemini 2.0 Flash scored below Gemini 1.5 Flash on this benchmark. The Table-as-Image setup degraded sharply on large, image-heavy tables.
- The data is released for evaluation only. The authors list scalability as the main limitation because curation depends on experts.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Prasham Yatinkumar Titiya, Jainil Trivedi, Chitta Baral and Vivek Gupta. 2026. MMTabReal: Real-World Benchmark for Multimodal Table Understanding. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{titiya2026mmtabreal,
  title = {{MMTabReal: Real-World Benchmark for Multimodal Table Understanding}},
  author = {Prasham Yatinkumar Titiya, Jainil Trivedi, Chitta Baral and Vivek Gupta},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.2047},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.2047}
}
```

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47. This section provides representative examples from each of the eight table structure types identified in Table 6, demonstrating the diversity and complexity of multimodal reasoning challenges across different structural configurations. *Figure 5: An example of single entity multimodal table in* the… [find](https://scholar.google.com/scholar?q=This+section+provides+representative+examples+from+each+of+the+eight+table+structure+types+identified+in+Table+6%2C+demonstrating+the+diversity+and+complexity+of+multimodal+reasoning+challenges+across+d)

*Updated 1 November 2026*