arXiv:2602.13812v3 Announce Type: replace-cross Abstract: Document-to-table (Doc2Table) extraction derives structured tables from unstructured documents under a target schema, enabling reliable and verifiable SQL-based data analytics. Although large language models (LLMs) have shown promise in flexible information extraction, their ability to produce precisely structured tables remains insufficiently understood, particularly for indirect extraction that requires complex capabilities such as reasoning and conflict resolution. Existing benchmarks neither explicitly distinguish nor comprehensivel
The proliferation of LLMs and the increasing need to extract structured data from unstructured documents for analytics is driving the creation of specialized benchmarks like DTBench.
This benchmark addresses a critical gap in evaluating LLMs' ability to accurately extract structured tables, which is fundamental for reliable data analytics and automating information processing.
The development of DTBench provides a more robust and explicit methodology for assessing LLMs in complex document-to-table extraction tasks, leading to more capable and verifiable AI systems.
- · AI developers
- · Data analytics platforms
- · Enterprise AI
- · Database providers
- · Manual data entry
- · Legacy OCR solutions
Improved performance and reliability of LLMs in structured data extraction from documents.
Accelerated automation of business processes that rely heavily on converting unstructured text into actionable data.
Enhanced trust and broader adoption of AI for critical data-driven decision-making across industries.
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Read at arXiv cs.AI