arXiv:2502. 15411v4 Announce Type: replace-cross Abstract: Accurate tagging of earnings reports can yield significant short-term returns for stakeholders.
By Rasmus Aavang, Giovanni Rizzi, Rasmus B{\o}ggild, Alexandre Iolov, Mike Zhang, Johannes Bjerva
arXiv:2607. 29677v1 Announce Type: new Abstract: Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata.
By Boyang Zhang, Adrian Lyjak, Eli Stewart, Zhaoqi Li, Simon Suo
arXiv:2607. 00008v1 Announce Type: cross Abstract: Extracting structured data from unstructured text using large language models (LLMs) becomes challenging when target schemas are large and complex.
By Sin Yu Bonnie Ho, Arlie Coles, Erik Larsson, Eric Marshall, Nathan Bodenstab, Paul Vozila
arXiv:2608. 06167v1 Announce Type: new Abstract: We present a schema-based framework for extracting complex, structured information from unstructured text documents using generative AI, followed by automated semantic evaluation of the extracted information against a gold standard.
By Modhurita Mitra, Jan-Willem Versteeg, Maarten D. Schermer, Shiva Nadi Najafabadi, Marie L. De Bruin, Lourens T. Bloem
arXiv:2607. 28662v1 Announce Type: new Abstract: Large language models extract entities and relationships from unstructured documents fluently but inconsistently: type vocabularies fracture across documents, the same person surfaces under several name variants, relationships duplicate, and distinct individuals who share a name risk silent conflation.
By Vaibhav Dangaich, Kevin Lewis, Kundeshwar Pundalik
arXiv:2607. 15879v1 Announce Type: cross Abstract: Much empirical legal research depends on translating unstructured text into structured variables.
By Jens Frankenreiter