arXiv AI
TEAR: Table Extraction with Attribute Recommendation from Texts via Large Language Models
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arXiv AI
Jul 2
SchemaRAG: Dynamic Large Schema Reduction for LLM-driven Structured Information Extraction
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 AI
Aug 5
ISEE: Interactive Semantic Enrichment for Database Fields
arXiv:2608. 02604v1 Announce Type: new Abstract: LLM-based agents are increasingly being deployed for data-related tasks, including data sense-making, exploration, and retrieval.
arXiv AI
Aug 7
Schema-Guided Hierarchical Information Extraction and Semantic Evaluation Using Generative AI
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 Machine Learning
Sep 23
Discovery-Driven Integration of Disjoint Tables via Text
arXiv:2609.26658v1 Announce Type: cross Abstract: Integrating heterogeneous datasets within data lakes is a critical challenge, particularly for semantically related tables that lack the explicit att...
arXiv AI
Aug 3
ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction
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.
arXiv Machine Learning
Sep 21
LLMs as Feature Engineers for Text-and-Tabular Prediction
The paper presents an iterative framework that uses large language models (LLMs) to automatically extract interpretable, schema‑bound categorical features from unstructured text for use in tabular prediction models. A generator LLM proposes semantic definitions, an extractor LLM materializes the features, and a downstream tabular model evaluates their predictive performance, with error‑driven natural‑language feedback guiding the search. Across three public datasets, the error‑driven loop speeds up feature discovery up to three times and the resulting features outperform any subset when combined with TF‑IDF and dense embeddings, while also providing instance‑level interpretability through SHAP importance rankings and a semantic audit trail.