Summary of Ai-assisted Generation Of Difficult Math Questions, by Vedant Shah et al.
AI-Assisted Generation of Difficult Math Questions
by Vedant Shah, Dingli Yu, Kaifeng Lyu, Simon Park, Jiatong Yu, Yinghui He, Nan Rosemary Ke, Michael Mozer, Yoshua Bengio, Sanjeev Arora, Anirudh Goyal
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 proposes a framework for generating diverse and challenging math questions by combining the strengths of large language models (LLMs) with human-in-the-loop approaches. The authors leverage LLM metacognition skills to extract core “skills” from existing math datasets, which are then used to generate novel questions that require reasoning across multiple skills. The pipeline involves iterative generation and refinement of questions and solutions through multiturn prompting, followed by human annotation and refinement. The resulting dataset, MATH, is shown to be higher-quality than the original MATH dataset, with models performing worse on MATH when using MATH^2 questions as in-context examples. The authors also observe a striking relationship between model performance on MATH and MATH, suggesting that solving MATH^2 questions requires a nontrivial combination of two distinct math skills. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create better math problems for big language models to solve. Currently, these models are great at answering simple math questions, but we need more challenging ones to help them learn and improve. The problem is that making these new questions is time-consuming and expensive if done by humans alone. This paper shows how we can use a combination of human and computer power to generate really hard math problems. We do this by taking the strengths of big language models, like their ability to understand certain skills or concepts, and using them to create new questions that require combining those skills in different ways. This makes it harder for even the best computers to solve the problems, which is exactly what we want to help them learn and get better. The result is a bigger dataset of really challenging math problems that will help big language models improve their math skills. |
Keywords
* Artificial intelligence * Prompting
