Automatically Generating Hard Math Problems from Hypothesis-Driven Error Analysis

1. 1Identify error-prone math concepts and skills in LLMs through hypothesis-driven error analysis
2. 2Develop an automatic benchmark generation method to create new math problems targeting these areas
3. 3Evaluate LLMs using the generated benchmarks to assess their mathematical capabilities and identify areas for improvement
4. 4Refine the benchmark generation method based on the evaluation results to create more challenging and relevant problems

ML researchers and AI engineers can benefit from this approach to improve LLMs' mathematical capabilities and identify error-prone areas, while data scientists can utilize the generated benchmarks to evaluate model performance

💡 Hypothesis-driven error analysis can be used to identify error-prone math concepts and skills in LLMs and generate targeted benchmarks to improve their mathematical capabilities

🤖 Automatically generating hard math problems for LLMs using hypothesis-driven error analysis 💡