Papers by Erik Larsson
SchemaRAG: Dynamic Large Schema Reduction for LLM-driven Structured Information Extraction (2026.acl-industry)
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| Challenge: | Structured information extraction (IE) pairs values from unstructured text with schema-defined keys. |
| Approach: | They propose a retrieval-augmented generation framework that prunes the output schema space for schema-conditioned information extraction tasks by leveraging schema metadata and few-shot examples. |
| Outcome: | The proposed framework can achieve up to 8.8% increase in micro-F1, 47% reduction in latency, and 48% reduction in token costs on real-world healthcare and e-commerce datasets. |