Summary of Artificial Intuition: Efficient Classification Of Scientific Abstracts, by Harsh Sakhrani et al.
Artificial Intuition: Efficient Classification of Scientific Abstracts
by Harsh Sakhrani, Naseela Pervez, Anirudh Ravi Kumar, Fred Morstatter, Alexandra Graddy Reed, Andrea Belz
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is developed to coarsely classify short scientific texts, such as grant or publication abstracts, for strategic insight or research portfolio management. The task is challenging due to brevity and the absence of context, but a Large Language Model (LLM) can provide metadata essential to the task, akin to augmenting supplemental knowledge representing human intuition. A workflow is proposed, and a pilot study uses a corpus of award abstracts from NASA. New assessment tools are developed in concert with established performance metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to group short scientific summaries into categories for easier understanding or research planning. This task is tricky because the texts are very short and don’t provide enough background information, but they show that using special computer models can help solve this problem. The method is tested on NASA’s award abstracts and includes developing new tools to measure how well it works. |
Keywords
» Artificial intelligence » Large language model
