UKRI

LLM-based pipeline for research entity extraction from UKRI grant proposals outperforms bespoke taxonomy approach

A UKRI-funded metascience project compares GPT-4o, Mistral, and a bespoke algorithm (DSIT-Taxonomies) for extracting and classifying research entities from funding proposal abstracts. Using a three-stage pipeline with Mistral as the primary extractor mapped against the OpenAlex Topics taxonomy, the LLM-based approach achieved 90.5% topic classification accuracy versus 71.4% for the DSIT-Taxonomies pipeline across 42 proposals. The authors conclude Mistral offers a practical, secure solution for large-scale analysis of sensitive grant data, with implications for identifying emerging research areas to guide public investment.