SchemaRAG Boosts LLM Structured Information Extraction Efficiency

Extracting structured data from unstructured text using large language models (LLMs) becomes increasingly difficult when dealing with extensive and intricate target schemas. Including the entire schema in the LLM prompt can lead to higher costs, increased latency, performance degradation due to "lost-in-the-middle" effects, and even exceed context length limitations. To overcome these challenges, a new retrieval-augmented generation (RAG) framework called SchemaRAG has been introduced. SchemaRAG intelligently prunes the output schema space dynamically for schema-conditioned information extraction tasks. It achieves this by effectively utilizing available schema metadata and few-shot examples. Evaluations conducted on real-world healthcare and e-commerce datasets demonstrated SchemaRAG's effectiveness. The framework achieved notable improvements, including an 8.8% increase in micro-F1 scores, a 47% reduction in latency, and a 48% reduction in token costs, proving its practical value for large-schema extraction scenarios.