LLM-Assisted Metadata Extraction

Parsers that combine structured prompts with deterministic post-processing to convert semi-structured documentation and tabular sources into stable, machine-readable metadata artifacts.

The approach grounds LLM outputs in explicit structures and inspectable intermediate representations, avoiding hallucination drift while still leveraging language model flexibility for parsing heterogeneous sources.

Part of work at DLR on AI-assisted transformation pipelines for scientific information systems within the Helmholtz Metadata Collaboration (HMC).