Summary of Comet: “cone Of Experience” Enhanced Large Multimodal Model For Mathematical Problem Generation, by Sannyuya Liu et al.
COMET: “Cone of experience” enhanced large multimodal model for mathematical problem generation
by Sannyuya Liu, Jintian Feng, Zongkai Yang, Yawei Luo, Qian Wan, Xiaoxuan Shen, Jianwen Sun
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes COMET, a novel approach for generating high-quality mathematical problems using a large multimodal model. The traditional method of separating problem-solving from generation is challenged by the limitations of monotonous data structures and homogeneous training objectives. To address this, the paper unifies stem generation and problem-solving into mathematical problem generation, and introduces a three-stage fine-tuning framework guided by the “Cone of Experience”. This framework divides fine-tuning data into symbolic, iconic, and direct experiences to mirror teacher career growth. The paper also constructs a Chinese multimodal mathematical problem dataset to fill a gap in this field. Experiments on multiple datasets verify the effectiveness of the proposed framework and model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to generate math problems using a big computer model that can understand many types of data. This is important because it could help with education, but there are some challenges that need to be addressed. The solution involves combining two processes into one and designing a special framework for training the model. The paper also makes its own dataset of Chinese math problems to fill a gap in this area. It tests the approach on multiple datasets and shows that it works well. |
Keywords
» Artificial intelligence » Fine tuning
