Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs
Apple Machine Learning Research
Glyph was introduced to automatically generate enterprise column descriptions and assign governed sensitivity ontology tags using cooperating, stateful LLM agents. The system fine-tuned a 6-layer MiniLM metadata encoder and improved same-tag retrieval NDCG@10 from 0.55 to 0.92 and MAP@100 from 0.19 to 0.90 versus the stock encoder. As a result, column documentation and classification become auditable and operable as a production service with provenance and graceful degradation.
Why it matters
Enterprise data lakes accumulate tables faster than human stewards can document or classify them, leaving columns with missing descriptions and unassigned governance labels. This documentation debt undermines data discovery, access control, and regulatory compliance. We present Glyph, a production system that frames two coupled problems, column description generation and column type annotation for data classification, as cooperating LLM agents orchestrated as stateful graphs. The Descriptor grounds generation in the pipeline source code that produces each column, retrieved on demand from an…
Related stories
Building a Multimodal RAG Pipeline with NVIDIA NeMo Retriever, Hosted NIMs, LanceDB, Reranking, and Grounded Generation
MarkTechPost · 1 month ago ·
41