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Multimodal Table Understanding

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arxiv 2406.08100 v1 pith:FRBMYUTO submitted 2024-06-12 cs.CL cs.AI

Mingyu Zheng , Xinwei Feng , Qingyi Si , Qiaoqiao She , Zheng Lin , Wenbin Jiang , Weiping Wang This is my paper
classification cs.CLcs.AI
keywords tablemodelmultimodalunderstandinggivenimageslanguagelarge
verification ladder T0 review T1 audit T2 compute T3 formal
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Although great progress has been made by previous table understanding methods including recent approaches based on large language models (LLMs), they rely heavily on the premise that given tables must be converted into a certain text sequence (such as Markdown or HTML) to serve as model input. However, it is difficult to access such high-quality textual table representations in some real-world scenarios, and table images are much more accessible. Therefore, how to directly understand tables using intuitive visual information is a crucial and urgent challenge for developing more practical applications. In this paper, we propose a new problem, multimodal table understanding, where the model needs to generate correct responses to various table-related requests based on the given table image. To facilitate both the model training and evaluation, we construct a large-scale dataset named MMTab, which covers a wide spectrum of table images, instructions and tasks. On this basis, we develop Table-LLaVA, a generalist tabular multimodal large language model (MLLM), which significantly outperforms recent open-source MLLM baselines on 23 benchmarks under held-in and held-out settings. The code and data is available at this https://github.com/SpursGoZmy/Table-LLaVA

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding

    cs.AI 2025-06 conditional novelty 6.0 of 10

    TableMoE is a multimodal table model whose role-aware router sends table tokens to HTML, JSON, and code experts and reports state-of-the-art results on its own WildStruct benchmarks and MMMU-Table.

  2. MathReal: We Keep It Real! A Real Scene Benchmark for Evaluating Math Reasoning in Multimodal Large Language Models

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    A new benchmark of 2,000 K-12 math questions photographed in real scenes finds that multimodal LLMs perform significantly worse on authentic handheld images than on clean benchmarks.

  3. Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A new 1,430-item multimodal benchmark shows that leading multimodal LLMs rarely notice small visual traps needed for commonsense safety reasoning, with top scores near 0.46 on a scale whose maximum is about 0.97.

  4. MMTABREAL: Real-World Benchmark for Multimodal Table Understanding

    cs.CV 2025-05 conditional novelty 5.0 of 10

    The paper releases a 500-table, 4,021-question benchmark of real-world multimodal tables and shows that leading vision-language models drop 20-40% in accuracy relative to earlier synthetic benchmarks.

  5. Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges

    cs.CL 2025-07 conditional novelty 3.0 of 10

    A structured review of table understanding with LLMs that proposes a taxonomy of input representations and identifies three research gaps.

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