Summary of Leveraging Large Language Models For Concept Graph Recovery and Question Answering in Nlp Education, by Rui Yang et al.
Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education
by Rui Yang, Boming Yang, Sixun Ouyang, Tianwei She, Aosong Feng, Yuang Jiang, Freddy Lecue, Jinghui Lu, Irene Li
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 This study explores the potential of Large Language Models (LLMs) in educational settings, specifically for domain-specific queries on concept graphs and question-answering (QA). Researchers assessed LLMs’ zero-shot performance in creating concept graphs and introduced TutorQA, a benchmark for scientific graph reasoning and QA. The pipeline CGLLM integrates concept graphs with LLMs to answer diverse questions, achieving competitive results with supervised methods. Human evaluation shows that generated answers have more fine-grained concepts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can understand and generate human-like text. But they’re not just good at talking – they’re also great at helping us learn! This study is all about how LLMs can be used in schools to help students find the answers to tricky questions. The researchers made a special test called TutorQA to see how well LLMs do on this kind of task. They found that LLMs are really good at creating concept graphs (which are like maps of what we’re talking about) and answering questions. This could be a big help for students who need extra support or just want to learn more efficiently. |
Keywords
» Artificial intelligence » Question answering » Supervised » Zero shot
