🦜 Mixture of Parrots: Experts improve memorization more than reasoning

This research paper investigates the effectiveness of Mixture-of-Experts (MoE) architectures in deep learning, particularly comparing their performance to standard dense transformers. The authors demonstrate through theoretical analysis and empirical experiments that MoEs excel at memory-intensive tasks, leveraging a large number of experts to effectively memorize data. However, for reasoning-based tasks, they find MoEs offer limited performance gains compared to dense models, suggesting that scaling the dimension of the model is more beneficial in such scenarios. The study provides valuable insights into the strengths and weaknesses of MoE architectures, highlighting their potential as memory machines while emphasizing the need for alternative approaches for tasks demanding strong reasoning capabilities.

📎 Link to paper