Practical AI Solutions for Document Instruction Data Evaluation
Challenges in Document Visual Question Answering (VQA)
Assessing the quality and efficacy of instruction datasets for large language models (LLMs) and multimodal large language models (MLLMs) in document VQA is a significant challenge. Existing methods focus primarily on text content, limiting their ability to comprehensively evaluate the datasets, thus impacting model performance in processing complex document data.
Limitations of Current Methods
Current methods like InsTag and Instruction-Following Difficulty (IFD) have limitations in assessing the diversity and complexity of instruction text, as well as in real-time practicality for model training. These limitations result in suboptimal model training outcomes, affecting performance in document VQA tasks.
Introducing ProcTag and DocLayPrompt
ProcTag, a novel data-oriented method, evaluates the efficacy of instruction datasets by focusing on the execution process of document instructions. It employs a structured method to model the instruction execution process and uses DocLayPrompt, a layout-aware prompting strategy, to enhance document representation. This innovative approach significantly improves the training efficiency and performance of LLMs and MLLMs in document VQA tasks.
Key Technical Aspects and Experimental Results
ProcTag represents documents using DocLayPrompt and utilizes GPT-3.5 to generate step-by-step pseudo-code for instruction execution, which is then tagged for diversity and complexity. The method outperforms existing methods like InsTag, achieving superior efficacy with only a subset of the data. Comprehensive experimental results demonstrate consistent improvements in model performance across different datasets and coverage rates, confirming the robustness and efficiency of the approach.
Advancements in AI for Document Understanding
ProcTag and DocLayPrompt address the limitations of existing text-based evaluation methods, offering a more accurate and efficient approach to training LLMs and MLLMs for document VQA. These innovations demonstrate significant improvements in data quality assessment and model performance, overcoming a critical challenge in document understanding.
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