Summary of Toward Reliable Ad-hoc Scientific Information Extraction: a Case Study on Two Materials Datasets, by Satanu Ghosh et al.
Toward Reliable Ad-hoc Scientific Information Extraction: A Case Study on Two Materials Datasets
by Satanu Ghosh, Neal R. Brodnik, Carolina Frey, Collin Holgate, Tresa M. Pollock, Samantha Daly, Samuel Carton
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 The paper investigates whether GPT-4 can perform ad-hoc schema-based information extraction from scientific literature using a basic prompting approach. The study focuses on replicating two existing material science datasets from original manuscripts, manually extracted by materials scientists. The authors assess the model’s performance and identify areas where it struggles to extract accurate information, providing insights for future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well GPT-4 can take scientific text and extract specific information about materials. They test this by trying to get the AI to recreate two existing datasets from scratch, using only the original papers. The scientists who created the original datasets then check the results to see where the AI went wrong, and suggest ways for future research to improve this important task. |
Keywords
» Artificial intelligence » Gpt » Prompting
