arXiv:2607.08539v1 Announce Type: cross Abstract: Leveraging large language models (LLMs) to analyze complex documents -- such as academic papers, technical manuals, and financial reports -- has emerged as a mainstream and critical task in both research and industry. In practice, users must first filter relevant documents from large collections and then conduct in-depth analysis (e.g. question answering) over the selected subset, yet existing systems flatten documents into plain-text chunks, discarding the rich hierarchical structures (sections, tables, figures, equations) and degrading downst
The proliferation of increasingly complex documents meets the rising demand for efficient information extraction from large language models, creating a pressure point for better parsing methods.
This development addresses a critical limitation in current LLM-based document analysis, potentially unlocking significantly greater utility and accuracy for complex information extraction across industries.
Existing systems that flatten documents into plain text will be rapidly superseded by hierarchical, structure-aware approaches, fundamentally changing how LLMs interact with and derive insights from structured information.
- · AI developers
- · Analytics software providers
- · Consulting firms
- · Research institutions
- · Legacy document analysis tools
- · Companies relying on manual data extraction
- · Simple plain-text LLM applications
Improved efficiency and accuracy in extracting insights from complex documents using LLMs.
Accelerated automation of knowledge work currently reliant on human interpretation of structured documents.
New forms of automated intelligence derived from cross-document hierarchical analysis, enabling sophisticated cross-domain insights without human intervention.
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Read at arXiv cs.AI