Scientific Field

Research on applying large language models (LLMs) to scientific literature focuses on improving information extraction, categorization, and knowledge base construction. Current efforts involve developing methods for automatically labeling scientific topics, enhancing LLMs with specialized scientific knowledge through fine-tuning and prompt engineering, and creating AI-assisted annotation systems to streamline the process of building comprehensive scientific knowledge bases. This work aims to accelerate scientific discovery by enabling more efficient analysis of vast amounts of scientific data and facilitating cross-disciplinary knowledge synthesis, ultimately improving research productivity and collaboration.