How to Get Your LLM to Generate Challenging Problems for Evaluation

The pace of evolution of Large Language Models (LLMs) necessitates new approaches for rigorous and comprehensive evaluation. Traditional human annotation is increasingly impracticable due to the complexities and costs involved in generating high-quality, challenging problems. In this work, we introduce CHASE, a framework to synthetically generate challenging problems using LLMs without human involvement. For a given task, our approach builds a difficult problem in a bottom-up manner from simpler components in a verifiable way. We implement CHASE to create evaluation benchmarks across three diverse domains on which state-of-the-art LLMs demonstrate severe vulnerabilities.