This podcast episode explores the journey of HuggingFace's evaluation practice, the significance of leaderboards in the AI community, and the limitations and biases in current evaluation methods. It emphasizes the need for reproducibility, unbiased evaluation metrics, and continuous benchmarking in the field of AI. The conversation also discusses the challenges of human evaluations, the role of prompts in model evaluations, and the limitations of compute resources. The episode concludes with a discussion on future improvements for the leaderboard and the excitement for upcoming evaluations.
