Read and Think: An Efficient Step-wise Multimodal Language Model for Document Understanding and Reasoning

• URL: http://arxiv.org/abs/2403.00816v3
• Date: Wed, 14 Aug 2024 07:26:08 GMT
• Title: Read and Think: An Efficient Step-wise Multimodal Language Model for Document Understanding and Reasoning
• Authors: Jinxu Zhang,
• Abstract summary: Existing document understanding models tend to generate answers with a single word or phrase directly. We use Multi-modal Large Language Models (MLLMs) to generate step-wise question-and-answer pairs for document images. We then use the generated high-quality data to train a humanized document understanding and reasoning model, dubbed DocAssistant.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the contents of multimodal documents is essential to accurately extract relevant evidence and use it for reasoning. Existing document understanding models tend to generate answers with a single word or phrase directly, ignoring the source document's evidence and lacking interpretability. In this work, we address the lack of step-wise capabilities through data augmentation and extension. Specifically, We use Multi-modal Large Language Models (MLLMs), which have strong visual understanding and reasoning abilities, as data generators to generate step-wise question-and-answer pairs for document images and use a high-performance LLM as the error detector to filter out noisy data. This step-wise data generation pipeline is implemented using both template-based and few-shot methods. We then use the generated high-quality data to train a humanized document understanding and reasoning model, specifically designed to solve complex questions that require reasoning or multi-hop question answering, dubbed DocAssistant. Experimental results demonstrate the effectiveness and application value of step-wise generation, showing a 5 improvement on InfoVQA with complex layouts and a 7 improvement on ChartQA with complex reasoning, compared to directly generated answers. We hope our work highlights the potential of synthetic data and encourages further exploration of multi-modal document reasoning capabilities.

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