Model Size Insights
The discussion highlights the complex relationship between model size and performance, with smaller models often yielding better results under fixed compute budgets. Fine-tuning presents challenges, such as the risk of catastrophic forgetting, where models may lose previously learned knowledge when adapting to new tasks. Continuous learning appears to enhance model efficiency, suggesting that ongoing user interaction could lead to improvements in performance over time.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Daniel Franzen & Jan Disselhoff - ARC Prize 2024 winners
Related Questions