Too long; didn't solve

1. 1Build a dataset of mathematical benchmarks with varying structural properties
2. 2Run experiments to evaluate model performance on the dataset
3. 3Configure the dataset to include adversarial examples
4. 4Test the relationship between prompt length and model performance
5. 5Apply the findings to improve model development and evaluation

AI engineers and researchers on a team benefit from understanding how to design and evaluate mathematical benchmarks to improve model performance, and data scientists can apply these insights to develop more effective testing datasets

💡 Prompt length and solution length significantly influence model behaviour on mathematical benchmarks

🤖 New research: structural properties of math benchmarks impact LLM performance #AI #LLMs