Emergence of PDF as a Dominant Format in the Digital Age

In recent years, the ever-evolving landscape of digital content management and dissemination has brought forth an intriguing development: the potential revival of PDF as a leading format for all things digital. Traditionally, PDFs have been both a blessing and a curse—offering a reliable way to share static documents across various platforms while posing significant challenges when trying to extract and structure data from them. However, thanks to recent advancements in artificial intelligence, particularly in the realm of language models, the longstanding complexity of converting PDF into structured content seems to be finally solved or at least on the brink of a breakthrough.

For years, one of the greatest challenges in dealing with PDFs was famously termed the "hamburger-to-cow" problem—essentially, how to take the polished, formatted output (the hamburger) and reconstruct the source data and structure (the cow) without losing essence or detail. Historically, this process required cumbersome and often inaccurate Optical Character Recognition (OCR) technologies to determine page structure and extract text. OCR was never foolproof and often left much to be desired in terms of accuracy and usability.

Enter the era of Large Language Models (LLMs), driven by an unprecedented need for machine-readable structured data from PDFs for training purposes. Technologies like Docling and Llama-Parse are pushing the boundaries, leveraging the power of LLMs to "read" PDFs in a way that mimics human interpretation. These innovations enable the extraction of both literal content and structural elements from PDFs without relying heavily on traditional OCR methods.

How does this work? By allowing LLMs to engage with PDFs, we're moving beyond basic character recognition to more comprehensive document understanding. For example, I've tried taking a screenshot of a text piece, uploading it to a large language model, and asking it to extract the text along with its structure. These models not only retrieved the text but also understood headings, subheadings, and other stylistic elements—proving their capability to think almost like a human when deciphering documents.

This shift suggests that a future where document formats evolve dynamically is within reach. We might soon see PDFs or similar formats emerge as "edge formats"—essentially not finalized until the moment of consumption. This approach could allow for documents to be formatted appropriately based on the end target, catering to both human readability and machine processing needs.

The implications of such advances are profound for scholarly communications, often burdened with preserving content in XML in repositories. With the potential to extract structured data from PDFs efficiently, the necessity of storing documents exclusively in XML for preservation could be re-evaluated.

In essence, these developments in LLM-driven PDF restructuring could signal a paradigm shift. PDF, or a variant thereof, might just become the universal format, adaptable enough for any context—making data organization more intuitive and content sharing more flexible. As we venture further into this digital frontier, it will be exciting to watch how these advancements will redefine the landscape of content formats and enter a new era of seamless digital document management.

• Docling arXiv Paper (2025) – “Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion.” Key details on Docling’s design, models, and integrations​.
• IBM Research Blog (Nov 2024) – “A new tool to unlock data from enterprise documents for generative AI.” Overview of Docling’s features, speed (30× OCR improvement), and real-world usage. ​
• LlamaIndex Documentation (2024) – LlamaParse Service Overview. Describes Llama-Parse as a LlamaIndex-created API for parsing files (PDF, Word, etc.) with generative AI techniques​.