[大模型实践] 卡比人贵时代的深度学习经验

Core Viewpoint - The article emphasizes the importance of developing new methodologies for large model experiments, focusing on key indicators, identifying true bottlenecks, balancing large and small experiments, and enhancing team collaboration [1]. Group 1: Key Indicators - Identifying key indicators is crucial as they should clearly differentiate between state-of-the-art (SoTA) models and others, guiding the direction of model iterations [4]. - Good indicators must objectively reflect performance levels and accurately indicate the direction for model improvements, avoiding the pitfalls of focusing on misleading metrics [4]. Group 2: Experimentation Methodologies - The cost of experiments has increased significantly, making it essential to conduct meaningful experiments rather than low-value ones [5]. - It is advised to conduct large experiments to identify significant issues while using small experiments to filter out incorrect ideas [6]. Group 3: Team Collaboration - Given the complexity of large model experiments, it is important for team members to understand their comparative advantages and roles within the team [8]. - Effective collaboration can be enhanced by finding ways to observe and document experiments together, increasing communication frequency [8].