Summary of Automatic Generation Of Question Hints For Mathematics Problems Using Large Language Models in Educational Technology, by Junior Cedric Tonga et al.
Automatic Generation of Question Hints for Mathematics Problems using Large Language Models in Educational Technology
by Junior Cedric Tonga, Benjamin Clement, Pierre-Yves Oudeyer
First submitted to arxiv on: 5 Nov 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 explores the use of Large Language Models (LLMs) as teachers to generate effective hints for students tackling math exercises designed for human high-school students. The study identifies error patterns made by simulated students, develops prompts for GPT-4o and Llama-3-8B-Instruct to generate hints, and evaluates their effectiveness in facilitating error correction. The results show that model errors increase with higher temperature settings, and that the best-performing prompts are tailored to specific errors or provide general hints based on common mathematical errors. Interestingly, Llama-3-8B-Instruct as a teacher showed better overall performance than GPT-4o. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses Large Language Models (LLMs) to help students with math exercises. It looks at how the models can be used to give helpful hints that correct mistakes and improve problem-solving skills. The researchers tested different approaches and found that some prompts work better than others in getting the LLMs to generate useful hints. They also looked at how well the LLMs did as students, and found that they improved with hints from GPT-4o. |
Keywords
» Artificial intelligence » Gpt » Llama » Temperature
