Artificial Intuition: Efficient Classification of Scientific Abstracts
<|reference_start|>Artificial Intuition: Efficient Classification of Scientific Abstracts: It is desirable to coarsely classify short scientific texts, such as grant or publication abstracts, for strategic insight or research portfolio management. These texts efficiently transmit dense information to experts possessing a rich body of knowledge to aid interpretation. Yet this task is remarkably difficult to automate because of brevity and the absence of context. To address this gap, we have developed a novel approach to generate and appropriately assign coarse domain-specific labels. We show that a Large Language Model (LLM) can provide metadata essential to the task, in a process akin to the augmentation of supplemental knowledge representing human intuition, and propose a workflow. As a pilot study, we use a corpus of award abstracts from the National Aeronautics and Space Administration (NASA). We develop new assessment tools in concert with established performance metrics.<|reference_end|>
@article{sakhrani2024artificial,
    title={Artificial Intuition: Efficient Classification of Scientific Abstracts},
    author={Harsh Sakhrani, Naseela Pervez, Anirudh Ravi Kumar, Fred Morstatter,
  Alexandra Graddy Reed, Andrea Belz},
    journal={arXiv preprint arXiv:2407.06093},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06093},
    primaryClass={cs.AI}
}