Evaluation Practice

Evaluation practices in various fields, from natural language processing to medical imaging, are undergoing significant scrutiny due to limitations in existing metrics and biases in established protocols. Current research focuses on developing more robust and informative evaluation methods, often tailored to specific tasks and addressing issues like dataset bias, the propagation of errors in modular systems, and the limitations of relying solely on automatic metrics. Improved evaluation practices are crucial for advancing the reliability and trustworthiness of AI systems across diverse applications, ensuring that model performance accurately reflects real-world capabilities and avoids unintended consequences.