Summary of Relations Prediction For Knowledge Graph Completion Using Large Language Models, by Sakher Khalil Alqaaidi et al.
Relations Prediction for Knowledge Graph Completion using Large Language Models
by Sakher Khalil Alqaaidi, Krzysztof Kochut
First submitted to arxiv on: 4 May 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 proposed approach leverages large language models and knowledge graph node names to improve relation prediction, a crucial task for completing incomplete knowledge graphs. By fine-tuning these models using node names only, the method achieves inductive settings capabilities and sets new scores on a widely used benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a way to use big language models and information about nodes in a special kind of graph called a knowledge graph. This helps with a problem called relation prediction, where we try to figure out what connections exist between different pieces of information. The idea is to use the names of these node points to teach the model new things, which lets it work well even when it hasn’t seen certain types of data before. In tests using a common benchmark, this approach performed better than other methods. |
Keywords
» Artificial intelligence » Fine tuning » Knowledge graph
