Real estate property listings expose structured metadata through the API. Still, the richest property-level information (i.
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arXiv:2502. 15411v4 Announce Type: replace-cross Abstract: Accurate tagging of earnings reports can yield significant short-term returns for stakeholders.
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.
The paper presents a two‑stage large language model pipeline for e‑commerce attribute extraction. First, it discovers a compact, ranked set of purchase‑discriminative attributes for each product category; second, it extracts those attribute values from catalog text using a fine‑tuned Qwen3‑4B model with Hyper‑Parallel Decoding. The approach attains 85% extraction accuracy while cutting inference costs by 92%, enabling scalable production use and producing consistent, comparable product knowledge bases.
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.
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.
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.