Testing Model

Testing machine learning (ML) models, particularly large language models (LLMs), is a rapidly evolving field focusing on moving beyond simple accuracy metrics to encompass broader system-level functionality and robustness. Current research emphasizes developing comprehensive testing protocols that address various aspects, including consistency across development stages, quality attributes beyond performance, and the use of techniques like fuzz testing to uncover unexpected failures. This work is crucial for ensuring the reliability and safety of increasingly prevalent ML-powered systems across diverse applications, from autonomous vehicles to healthcare.