LLMs Automate Scientific Text Categorization via Prompt-Chaining
A study on arXiv (2604.23430) evaluates off-the-shelf Large Language Models (LLMs) for automatic categorization of scientific texts using in-context learning and prompt-chaining. The researchers employed the hierarchical ORKG taxonomy as a classification framework and the FORC dataset as ground truth. The work addresses challenges in navigating the growing volume of scientific literature, aiming to enhance research information systems beyond keyword search. The study systematically tests LLM performance in classifying texts according to a given scheme, contributing to automated content categorization in academia and industry.
Key facts
- Study evaluates LLMs for scientific text categorization
- Uses in-context learning and prompt-chaining
- Employs hierarchical ORKG taxonomy
- FORC dataset used as ground truth
- Published on arXiv (2604.23430)
- Aims to improve research information systems
- Addresses challenges of growing scientific literature
- Focuses on automatic content categorization
Entities
Institutions
- arXiv
- ORKG