arXiv:2604.09237v2 Announce Type: replace Abstract: Many disciplines pose natural-language research questions over large document collections whose answers typically require structured evidence, traditionally obtained by manually designing an annotation schema and exhaustively labeling the corpus, a slow and error-prone process. We introduce ScheMatiQ, which leverages calls to a backbone LLM to take a question and a corpus to produce a schema and a grounded database, with a web interface that lets steer and revise the extraction. In collaboration with domain experts, we show that ScheMatiQ yie
The development of sophisticated large language models (LLMs) and the increasing need to extract structured data from vast unstructured text drive the timing of this innovation.
This breakthrough automates and streamlines the arduous process of manual data schema design and labeling, significantly accelerating research and data-driven decision-making across many disciplines.
The critical bottleneck of generating structured evidence from natural language research questions, traditionally slow and error-prone, is now significantly mitigated through interactive, AI-driven schema discovery.
- · Researchers and academics
- · Data scientists and analysts
- · LLM developers
- · Analytics and B.I. sector
- · Manual data annotation services
- · Traditional data extraction software relying on strict rule sets
Increased efficiency in knowledge extraction from large document corpora.
Faster research cycles and the ability to test more hypotheses with structured data.
New insights and discoveries emerge from previously inaccessible or labor-intensive data analysis, accelerating scientific and commercial innovation.
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Read at arXiv cs.CL