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. In such cases, including the full schema in the prompt increases cost and latency, risks lost-in-the-middle performance degradation, and can exceed context length limits. We propose SchemaRAG, a retrieval-augmented generation (RAG) framework that dynamically prunes the output schema space for schema-conditioned information extraction tasks by leveraging schema metadata and few-shot examples when ava
The rapid advancement and increased complexity of large language models are creating urgent needs for more efficient and cost-effective methods of structured information extraction.
This development addresses critical limitations in LLM applications like context window constraints and computational overhead, making them more practical and scalable for complex data tasks.
LLMs can now perform structured information extraction more efficiently from unstructured text, even with large and complex schemas, reducing costs and improving reliability.
- · AI developers
- · Data-intensive industries
- · SaaS providers leveraging LLMs
- · Research institutions
- · Brute-force LLM integration strategies
- · Manual data extraction services
SchemaRAG directly improves the performance and cost-efficiency of LLMs for information extraction tasks.
Enhanced information extraction will accelerate the automation of complex workflows currently requiring human interpretation, especially in enterprise settings.
The increased efficiency could enable new classes of AI agents capable of autonomously processing and structuring vast amounts of disparate information, potentially collapsing certain white-collar workflows.
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Read at arXiv cs.AI