Summary of Assessing Llms Suitability For Knowledge Graph Completion, by Vasile Ionut Remus Iga and Gheorghe Cosmin Silaghi
Assessing LLMs Suitability for Knowledge Graph Completion
by Vasile Ionut Remus Iga, Gheorghe Cosmin Silaghi
First submitted to arxiv on: 27 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 abstract presents research on the capabilities of Large Language Models (LLMs) in solving tasks related to Knowledge Graphs. While LLMs have shown promise in completing knowledge graphs, they often hallucinate answers or produce non-deterministic results, leading to incorrect responses. To explore opportunities and challenges in this area, the authors experimented with three distinct LLMs on a static knowledge graph completion task using prompts constructed from the TELeR taxonomy. The study evaluated the models’ performance in Zero- and One-Shot contexts, demonstrating that they can be effective if prompted with sufficient information and relevant examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can help complete puzzles about things we know. But sometimes, they make mistakes by giving wrong answers. This paper looks at how well these models do on a specific task called “completing knowledge graphs”. Knowledge graphs are like maps of information we have. The researchers tested three special LLMs to see if they could do this job correctly. They used special instructions and examples to help the models understand what they wanted them to do. The results showed that these models can do a good job if given the right clues. |
Keywords
» Artificial intelligence » Knowledge graph » One shot
