Evaluation Protocol

Evaluation protocols are crucial for assessing the performance of machine learning models, particularly in complex domains like conversational AI, large language models, and video captioning. Current research emphasizes the need for more comprehensive and robust protocols that account for both system-centric metrics (e.g., accuracy, fluency) and user-centric factors (e.g., usability, relevance), addressing issues like data contamination and inherent randomness in evaluation procedures. This focus on improved evaluation methodologies is vital for ensuring fair comparisons between models, identifying areas for improvement, and ultimately accelerating progress in these rapidly evolving fields.