Summary of Construction Of Hyper-relational Knowledge Graphs Using Pre-trained Large Language Models, by Preetha Datta et al.
Construction of Hyper-Relational Knowledge Graphs Using Pre-Trained Large Language Models
by Preetha Datta, Fedor Vitiugin, Anastasiia Chizhikova, Nitin Sawhney
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces a novel method for extracting hyper-relations from text, using OpenAI’s GPT-3.5 model and zero-shot prompts. This approach enables the construction of comprehensive knowledge graphs without requiring explicit supervision. The method is compared to a baseline, achieving a promising recall of 0.77. While precision is currently lower, the analysis of model outputs identifies potential avenues for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a way to find connections between things using artificial intelligence and text. It uses a special type of AI called GPT-3.5 and doesn’t need any training data. This helps create detailed networks of information that are hard to make by hand. The method is tested against a baseline and does well, finding most relevant relationships. Although it’s not perfect yet, the results show where future improvements can be made. |
Keywords
» Artificial intelligence » Gpt » Precision » Recall » Zero shot
