Interactive Evaluation

Interactive evaluation focuses on assessing the performance of AI systems, particularly large language models (LLMs), through direct human interaction rather than relying solely on static metrics. Current research emphasizes developing robust and efficient evaluation frameworks, including those incorporating user simulators and dynamic human annotation strategies, to address limitations of existing automated methods and better capture nuanced aspects of system performance in complex tasks like dialogue and code interpretation. These advancements are crucial for improving the reliability and trustworthiness of AI systems across diverse applications, from chatbot development to automated design tools.