Artificial Intuition: Efficient Classification of Scientific Abstracts

• URL: http://arxiv.org/abs/2407.06093v1
• Date: Mon, 8 Jul 2024 16:34:47 GMT
• Title: Artificial Intuition: Efficient Classification of Scientific Abstracts
• Authors: Harsh Sakhrani, Naseela Pervez, Anirudh Ravi Kumar, Fred Morstatter, Alexandra Graddy Reed, Andrea Belz,
• Abstract summary: Short scientific texts efficiently transmit dense information to experts possessing a rich body of knowledge to aid interpretation. 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.
• Score: 42.299140272218274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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