Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding (2505.05446v1)

Abstract: Visual Document Understanding has become essential with the increase of text-rich visual content. This field poses significant challenges due to the need for effective integration of visual perception and textual comprehension, particularly across diverse document types with complex layouts. Moreover, existing fine-tuning datasets for this domain often fall short in providing the detailed contextual information for robust understanding, leading to hallucinations and limited comprehension of spatial relationships among visual elements. To address these challenges, we propose an innovative pipeline that utilizes adaptive generation of markup languages, such as Markdown, JSON, HTML, and TiKZ, to build highly structured document representations and deliver contextually-grounded responses. We introduce two fine-grained structured datasets: DocMark-Pile, comprising approximately 3.8M pretraining data pairs for document parsing, and DocMark-Instruct, featuring 624k fine-tuning data annotations for grounded instruction following. Extensive experiments demonstrate that our proposed model significantly outperforms existing state-of-theart MLLMs across a range of visual document understanding benchmarks, facilitating advanced reasoning and comprehension capabilities in complex visual scenarios. Our code and models are released at https://github. com/Euphoria16/DocMark.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding

The paper introduces an advanced pipeline designed to overcome the challenges in Visual Document Understanding (VDU) through the adaptive generation of markup languages. The authors address the limitations in current practices by offering a method that integrates visual perception with textual comprehension within diverse document types featuring complex layouts. They propose a comprehensive and structured dataset creation process to enhance document parsing and understanding capabilities fundamentally.

The central focus of the paper is an innovative approach that employs the adaptive generation of markup languages—such as Markdown, JSON, HTML, and TiKZ—to create detailed document representations that permit contextually-grounded responses. This methodology is exemplified by the development of two significant datasets: DocMark-Pile and DocMark-Instruct. DocMark-Pile consists of approximately 3.8 million pretraining data pairs tailored for document parsing, while DocMark-Instruct includes 624,000 fine-tuning data annotations intended for contextually-grounded instruction following.

The experimental results presented in the paper demonstrate that this approach considerably surpasses existing state-of-the-art Multimodal LLMs (MLLMs) across numerous VDU benchmarks. This is achieved by facilitating more advanced reasoning and comprehension capabilities within complex visual scenarios.

Key Contributions

1. Adaptive Use of Markup Languages: The paper suggests a pipeline that applies various markup languages adaptively to bridge the gap between visual inputs and linguistic understanding. This choice enhances the comprehension capabilities of models across diverse document formats.
2. Novel Dataset Contributions: By introducing DocMark-Pile and DocMark-Instruct, the authors offer a structured approach to document parsing and contextually grounded instruction following—enabling the model to handle complex document formats such as Plain Text, Markdown, LaTeX, HTML, JSON, and TiKZ more effectively.
3. Superior Model Performance: The authors’ models outperformed existing MLLMs on several challenging document understanding tasks. Notably, their work showed remarkable improvements in text recognition benchmarks and structural information extraction tasks, thereby validating the efficacy of the proposed methodology.

Implications and Future Directions

From a theoretical perspective, this research contributes to the understanding of how structured data and natural language intricacies can be systematically represented and comprehended by AI systems. Practically, the implications include enhanced performance in applications requiring document parsing and interpretation, such as automated business processing and document archiving systems, which benefit from reduced hallucinations and improved spatial relationship understanding.

The paper underlines the necessity for adaptive, context-aware solutions in future AI developments, suggesting further exploration into expanding this approach to other AI domains requiring multimodal comprehension. Potential future work could investigate optimizing computational costs related to increased token usage due to adaptive context generation, aiming to balance efficiency with performance gains. Additionally, exploring integration with other emerging AI paradigms and expanding the contextual understanding to broader application contexts could provide fruitful research avenues.

Overall, the research offers significant advancements in VDU and provides a strong foundation for future exploration into AI's capability to understand, interpret, and effectively utilize complex information from varied document formats.