Brittlebench: Quantifying LLM robustness via prompt sensitivity

1. 1Develop a theoretical framework for quantifying model robustness
2. 2Create a benchmark to evaluate LLMs' sensitivity to prompt variations
3. 3Test and refine the framework using real-world user inputs with noise and variability
4. 4Apply the framework to improve LLM performance and reliability

AI researchers and engineers benefit from this framework as it helps them evaluate and improve the robustness of their language models, while product managers can use it to inform design decisions for more reliable AI-powered products

💡 Evaluating LLMs using static benchmarks can overestimate their true performance, and prompt sensitivity is a key factor in determining robustness

🚀 Introducing Brittlebench: a framework to quantify LLM robustness via prompt sensitivity