Brick-DICL applies dynamic in-context learning with two RAG stages and multi-LLM filtering to automate mapping of BMS points to the 936-class Brick ontology, claiming accuracy gains and reduced manual verification.
Learning from generalization patterns: An evaluation-driven approach to en- hanced data augmentation for fine-tuning small language models
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Brick-DICL: Dynamic In-Context Learning for Automated Brick Schema Classification
Brick-DICL applies dynamic in-context learning with two RAG stages and multi-LLM filtering to automate mapping of BMS points to the 936-class Brick ontology, claiming accuracy gains and reduced manual verification.