Multimodal Table Understanding (2406.08100v1)

Published 12 Jun 2024 in cs.CL and cs.AI

Abstract: Although great progress has been made by previous table understanding methods including recent approaches based on 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 LLM (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

Authors (7)

Mingyu Zheng (5 papers)
Xinwei Feng (12 papers)
Qingyi Si (23 papers)
Qiaoqiao She (9 papers)
Zheng Lin (104 papers)
Wenbin Jiang (18 papers)
Weiping Wang (123 papers)

Citations (5)

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Summary

Multimodal Table Understanding

The paper "Multimodal Table Understanding" introduces a novel problem of directly understanding tables from images without transforming them into textual representations. This problem addresses the challenge of table comprehension in real-world scenarios where high-quality textual representations are often inaccessible, but visual table representations are readily available.

Proposed Problem and Dataset

The paper defines the multimodal table understanding problem, where models need to generate correct responses to various table-related tasks based solely on table images. To support the development and evaluation of these models, the authors construct a large-scale dataset named MMTab. MMTab covers a wide range of table images, instructions, and tasks. This dataset is divided into three parts:

MMTab-pre: 150K table recognition samples used for pre-training.
MMTab-instruct: 232K samples for instruction tuning across 14 table-based tasks.
MMTab-eval: 49K test samples for evaluation, including both held-in and held-out benchmarks.

The MMTab dataset is enriched with diversities such as varying table structures and styles, including Web-page, Excel, and Markdown formats.

Table-LLaVA Model

To tackle the multimodal table understanding problem, the authors develop Table-LLaVA, a tabular multimodal LLM (MLLM). This model employs an enhanced two-stage training paradigm:

Pre-training: LLaVA-1.5 is pre-trained using table recognition tasks on MMTab-pre. The objective is to align the visual features of table images with their textual representations, enhancing the model's ability to perceive table structures and content.
Instruction fine-tuning: The pre-trained model is fine-tuned with diverse multimodal tasks from MMTab-instruct, equipping it to follow instructions related to table-based tasks.

Experimental Results

The paper presents an extensive evaluation of Table-LLaVA against several open-source models and GPT-4V, tested on 23 benchmarks. Table-LLaVA demonstrates superior performance over existing MLLM baselines and exhibits competitive results with the closed-source GPT-4V on various tasks. Key findings include:

Table-LLaVA outperforms other open-source MLLMs: The model shows significant improvements in capabilities such as table recognition, question answering, fact verification, and text generation from table images.
Performance in table structure understanding tasks: Table-LLaVA excels in basic tasks like table size detection (TSD), table cell extraction (TCE), and merged cell detection (MCD).
Generalization on held-out benchmarks: The model maintains strong performance on unseen tasks, indicating robust generalization abilities.

Implications and Future Directions

The research presents a comprehensive approach to multimodal table understanding, highlighting both practical and theoretical implications. Practically, it opens avenues for developing applications that can interpret tables directly from images, useful in situations where textual extraction is challenging, such as scanned documents and screenshots. Theoretically, the work bridges gaps in current LLMs by providing foundational enhancements in table structure comprehension.

Future developments in AI related to this research may include:

Improved OCR capabilities: Enhancing optical character recognition to better support multimodal table understanding.
Expansion to multi-table and multi-document scenarios: Extending the model's ability to handle multiple tables and integrate information across documents.
Incorporation of multilingual support: Broadening the language scope of MMTab and corresponding models to handle non-English tables.
Integration with external tools: Combining Table-LLaVA with tools such as Python interpreters for advanced mathematical reasoning and data manipulation tasks.

In conclusion, the paper establishes a solid foundation for multimodal table understanding, proposing a coherent problem definition, contributing an extensive dataset, and presenting a proficient model. The findings and resources from this research are set to catalyze further advancements in the field, bridging practical application needs and academic inquiries.