Multimodal Table Understanding (AI summary)

Key Points

1. The study addresses the challenge of directly understanding tables using visual information, proposing a new problem: multimodal table understanding, which requires models to generate correct responses to various table-related requests based on given table images.

2. The researchers construct a large-scale dataset named MMTab, which encompasses a wide spectrum of table images, instructions, and tasks, including 150K table recognition samples for pre-training, 232K samples of 14 table-based tasks for instruction tuning, and 49K test samples composing 17 held-in and 7 held-out benchmarks. The dataset covers tables of diverse structures, styles, and domains, and encompasses a wide range of tabular tasks.

3. Based on the curated dataset, the researchers develop a versatile tabular multimodal large language model (MLLM) named Table-LLaVA, which significantly outperforms recent open-source MLLM baselines on 23 benchmarks under held-in and held-out settings.

4. The study compares Table-LLaVA with a series of open-source MLLMs and closed-source GPT-4V, and demonstrates that Table-LLaVA beats strong MLLM baselines on 17 held-in and 6 held-out benchmarks and is competitive with the powerful GPT-4V on 14 benchmarks with a subset of test samples.

5. The authors also propose and construct multimodal table structure understanding tasks that have been overlooked in previous studies and reveal the need for understanding diverse table structures and colored table elements using visual information.

6. The research finds that existing table-oriented MLLMs rely heavily on the conversion of tables into text sequences and that previous tabular LLMs are unable to directly understand table images, limiting their potential application scenarios.

7. The study outlines the contribution of the research, including the systematic exploration of the multimodal table understanding problem, the construction and release of a large-scale dataset MMTab, and the development of a versatile tabular MLLM Table-LLaVA that significantly outperforms a range of strong MLLM baselines under both held-in and held-out settings.

8. The findings indicate that MMTab offers advantages such as a large volume of data, including tables of diverse structures, styles, and domains, and the dataset encompasses a wide range of tabular tasks. The researchers demonstrate that the constructed data can supplement the missing table understanding capability of the current MLLMs and contribute to improving the generalization of the resulting models.

9. The study concludes by emphasizing the importance of research on multimodal table understanding from the task, dataset, and model perspectives, with potential applications in various industries and fields such as financial analysis, scientific research, and government reporting.

Summary

The research paper proposes a new problem called "multimodal table understanding" where the goal is for a model to generate responses to table-related requests based on table images. Current table understanding methods rely on converting tables into text sequences, which is not always feasible in real-world scenarios where table images are more accessible.

To facilitate research on this new problem, the paper introduces a large-scale dataset called MMTab. MMTab contains over 150,000 samples for pre-training and 232,000 samples for instruction tuning, covering a wide range of table structures, styles, and downstream tasks. The dataset includes novel table structure understanding tasks that have been overlooked in previous studies.

Based on MMTab, the paper develops a versatile tabular multimodal large language model called Table-LLaVA. Table-LLaVA is trained in two stages - first with a table recognition pre-training task to align visual and textual table representations, and then with instruction tuning on the diverse table-based tasks in MMTab.

Experiments show that Table-LLaVA significantly outperforms existing open-source multimodal language models on 17 held-in and 6 held-out benchmarks. It is even competitive with the powerful GPT-4V model on 14 benchmarks. Ablation studies further validate the effectiveness of the proposed dataset and training strategy.

The paper concludes that this work establishes a strong foundation for future research on multimodal table understanding and can help facilitate the development of more general multimodal language models.

Reference: https://arxiv.org/abs/2406.08100