Summary of Knowledge Graphs As Context Sources For Llm-based Explanations Of Learning Recommendations, by Hasan Abu-rasheed et al.
Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations
by Hasan Abu-Rasheed, Christian Weber, Madjid Fathi
First submitted to arxiv on: 5 Mar 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 for generating high-precision explanations for personalized education is proposed, combining large language models (LLMs) and knowledge graphs (KGs). LLMs have shown promise in generating human-like explanations, but their precision remains a concern in sensitive fields like education. To address this issue, the authors utilize KGs as factual context sources, reducing the risk of model hallucinations and wrong information. Domain experts are integrated into the prompt engineering phase to ensure relevance. The approach is evaluated using Rouge-N and Rouge-L measures, as well as qualitative feedback from experts and learners. Results show enhanced recall and precision compared to solely relying on GPT models, with a reduced risk of imprecise information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make learning more understandable by creating explanations for education recommendations. They use special language models that can sound like humans, but these models are not very precise yet. To fix this problem, the authors combine these models with knowledge graphs that have lots of facts. This helps prevent mistakes and ensures the explanations are relevant to the learner. Experts helped make sure the explanations were good. The approach was tested and showed better results than using just one language model. |
Keywords
» Artificial intelligence » Gpt » Language model » Precision » Prompt » Recall » Rouge
