Practical Solutions and Value of Docmatix: A Dataset for Document Visual Question Answering
Challenges in DocVQA
Document Visual Question Answering (DocVQA) faces challenges due to the complexity of collecting and annotating data from various document formats. Domain-specific differences, privacy concerns, and the lack of document-structure uniformity further complicate dataset development.
Importance of DocVQA Datasets
Despite the challenges, the need for DocVQA datasets is crucial for enhancing model performance, benchmarking, and automating document-related processes across sectors.
Introduction of Docmatix
The new monumental Docmatix dataset contains 2.4 million pictures and 9.5 million Q/A pairs, extracted from 1.3 million PDF documents. This significant scale showcases the potential impact of Docmatix on document accessibility.
Creation and Validation of Docmatix
The researchers used a Phi-3-small model to create Q/A pairs from the PDFA transcriptions and validated the dataset by removing hallucinated Q/A pairings. The processed images are now easily accessible to users, ensuring the dataset’s reliability.
Improving Model Performance with Docmatix
The researchers conducted ablation experiments to fine-tune the prompts and assess Docmatix’s performance. Training on a small subset of Docmatix showed a relative improvement of about 20% in model performance, highlighting the dataset’s potential impact on model training.
Future of DocVQA Models
The team encourages the open-source community to use Docmatix to reduce the disparity between proprietary and open-sourced Vision-Language Models (VLMs) and to train new, high-performing DocVQA models.
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