LLM Information Extraction
LLM: Large Language Model
Overview
This field studies how to turn text, tables, images, and long documents into structured data that can support search, analysis, and decision-making. Common tasks include named-entity and relation extraction, event and attribute extraction, clinical report parsing, and building knowledge graphs from scientific or legal literature. Approaches range from prompting and retrieval-augmented examples to fine-tuning, schema-constrained decoding, agent workflows, and verification against source evidence. Models can produce well-formed outputs, but accurate and complete values remain difficult in long, noisy, multimodal, or specialized documents, so evaluation, provenance, and human review remain central.
Latest papers
238 papers31 papers in the last four weeks, down 3% on the four weeks before. 0.2% of all new papers.