arXiv:2606.30304v1 Announce Type: cross Abstract: This paper presents preliminary findings from a UKRI-funded Metascience project comparing three LLM-based approaches, GPT-4o, Mistral, and a bespoke algorithm, DSIT-Taxonomies, for extracting and classifying research entities from funding proposals. Our project "Tracking Stars and Unicorns" aims to identify early signals of emerging research areas to inform public investment. Our methodology employed a three-stage pipeline, leveraging Mistral for primary entity extraction and mapping against the OpenAlex Topics taxonomy. We evaluated our approa
The rise of advanced LLMs enables new capabilities for analysing complex, unstructured data like grant proposals, making this type of project feasible and timely.
This initiative signifies a government's proactive use of AI to identify emerging research trends and strategically allocate funding, potentially shaping future scientific and technological landscapes.
Governments can now leverage sophisticated AI tools to gain earlier insights into nascent research areas, moving beyond traditional, potentially slower, human-driven analysis.
- · UKRI
- · AI/Metascience researchers
- · Emerging research areas
- · Governments adopting AI for strategic insights
- · Traditional analysis methods
- · Research areas not identified by AI models
Government funding agencies will adopt AI tools for more efficient and foresightful research investment strategies.
Nations that successfully implement such systems could gain a competitive advantage in identifying and fostering critical technological advancements.
The development of 'soft power' through scientific leadership could be influenced by AI-driven funding decisions, impacting global innovation hubs.
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Read at arXiv cs.AI