Summary of Developing a Tutoring Dialog Dataset to Optimize Llms For Educational Use, by Menna Fateen et al.
Developing a Tutoring Dialog Dataset to Optimize LLMs for Educational Use
by Menna Fateen, Tsunenori Mine
First submitted to arxiv on: 25 Oct 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 This paper explores the potential of smaller language models for one-on-one tutoring in reading comprehension. The authors developed a synthetic dataset and fine-tuned a smaller model using this dataset, comparing its performance to a larger model in real-world scenarios. The results show that the smaller model performs similarly to the larger model but at a lower cost, suggesting a viable approach for implementing language-based tutoring systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers used large language models to create a tutoring system for reading comprehension problems. They made a smaller version of the model and tested it against a bigger one. The smaller model did just as well, but was cheaper to make. This could be a good way to make tutoring more accessible and affordable. |
