ProcTag: Process Tagging for Assessing the Efficacy of Document Instruction Data (2407.12358v1)

Abstract: Recently, LLMs and multimodal LLMs (MLLMs) have demonstrated promising results on document visual question answering (VQA) task, particularly after training on document instruction datasets. An effective evaluation method for document instruction data is crucial in constructing instruction data with high efficacy, which, in turn, facilitates the training of LLMs and MLLMs for document VQA. However, most existing evaluation methods for instruction data are limited to the textual content of the instructions themselves, thereby hindering the effective assessment of document instruction datasets and constraining their construction. In this paper, we propose ProcTag, a data-oriented method that assesses the efficacy of document instruction data. ProcTag innovatively performs tagging on the execution process of instructions rather than the instruction text itself. By leveraging the diversity and complexity of these tags to assess the efficacy of the given dataset, ProcTag enables selective sampling or filtering of document instructions. Furthermore, DocLayPrompt, a novel semi-structured layout-aware document prompting strategy, is proposed for effectively representing documents. Experiments demonstrate that sampling existing open-sourced and generated document VQA/instruction datasets with ProcTag significantly outperforms current methods for evaluating instruction data. Impressively, with ProcTag-based sampling in the generated document datasets, only 30.5\% of the document instructions are required to achieve 100\% efficacy compared to the complete dataset. The code is publicly available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/ProcTag.

• The paper introduces process tagging to evaluate document instruction datasets by focusing on the execution process rather than just textual content.
• It employs DocLayPrompt, a layout-aware strategy that integrates OCR and layout detection for enhanced document representation.
• Results demonstrate that ProcTag achieves 30.5% data efficiency compared to full datasets, offering a more effective evaluation approach.

An Expert Overview of "ProcTag: Process Tagging for Assessing the Efficacy of Document Instruction Data"

The proliferation of LLMs and multimodal LLMs (MLLMs) has significantly advanced the capabilities of document visual question answering (VQA) systems. The paper "ProcTag: Process Tagging for Assessing the Efficacy of Document Instruction Data" by Yufan Shen et al. addresses the problem of evaluating document instruction datasets, which is crucial for training effective LLMs and MLLMs in this domain. Traditional methods for evaluating these datasets have relied heavily on the textual content of the instructions, a limitation that this paper seeks to overcome through the innovative approach of process tagging.

Summary of Contributions

The authors propose ProcTag, a novel data-oriented method that focuses on the execution process of instructions rather than their textual content to assess the efficacy of document instruction datasets. This approach allows for a more comprehensive evaluation by considering the diversity and complexity of the execution tags. Several key contributions emerge from this work:

1. Process Tagging: ProcTag tags the document instruction dataset based on the instruction execution process rather than the instruction text. This innovative approach captures the complexity and diversity of instruction types more effectively.
2. DocLayPrompt: The paper introduces a semi-structured layout-aware document prompting strategy known as DocLayPrompt to better represent documents. This strategy enriches document representation by integrating layout information, which is vital for accurately modelling instruction execution processes.
3. Efficacy of ProcTag: The authors demonstrate through experiments that ProcTag-based sampling outperforms existing evaluation methods. They show that with ProcTag, only 30.5% of document instructions from the generated datasets are required to achieve the same efficacy as the complete dataset, highlighting significant efficiency gains.

Detailed Analysis

Document Representation

Effective representation of document content is a cornerstone of ProcTag. Using OCR and layout detection tools, DocLayPrompt captures both textual content and layout information, providing a comprehensive view of the document structure. This representation is crucial for accurately modeling the instruction execution process.

Instruction Execution Process Generation

ProcTag utilizes the chain-of-thought reasoning capability of GPT-3.5 to generate the instruction execution process, which is then expressed in pseudo-code. This approach ensures that the instruction execution process is both precise and easily interpretable. The generation consists of a step-by-step description followed by the corresponding pseudo-code, ensuring that the process is detailed and logically coherent.

Process Tagging and Efficacy Assessment

Tags derived from the instruction execution process are used to measure the complexity and diversity of the dataset. The tagging process is meticulous, involving function name extraction, frequency filtering, and aggregation to ensure that the tags are both unique and relevant. The effectiveness of this method is validated by comparing ProcTag to other data sampling methods like InsTag and random sampling, with ProcTag showing superior performance.

Implications and Future Developments

The research presented in this paper has both practical and theoretical implications. Practically, the ability to assess the efficacy of document instruction datasets more accurately can lead to the creation of more efficient training datasets, saving both time and computational resources. Theoretically, this work opens new avenues for exploring the role of the instruction execution process in other types of AI tasks beyond document VQA.

Future developments may involve extending the methodology to cover a wider range of AI domains and exploring the use of multimodal inputs for even more comprehensive document representation. Additionally, the application of more advanced models like GPT-4V for this purpose could further enhance the granularity and effectiveness of the tagging process.

Conclusion

"ProcTag: Process Tagging for Assessing the Efficacy of Document Instruction Data" presents a significant advancement in the evaluation of document instruction datasets. By shifting the focus from the textual content of instructions to the execution process, the authors provide a more robust and efficient method for dataset assessment. The introduction of DocLayPrompt for better document representation and the demonstrated efficacy of ProcTag in various experimental setups highlights the potential of this approach to significantly impact the training and performance of LLMs and MLLMs in document VQA tasks.