新闻
On the Importance of Gating: Memorization vs. In-Context Learning in State Space Models
Apple Machine Learning Research · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Apple researchers show that gating mechanisms in State Space Models cause them to learn memorization before in-context learning, explaining their lag behind Transformers.
- 为何重要
- Engineers building or optimizing SSMs for language modeling need to understand how gating affects training dynamics and generalization on retrieval tasks.
- 注意
- Gating helps long-sequence generalization but hurts in-context learning convergence. The findings are theoretical and experimental but don't yet propose production solutions.
- language model
- retrieval
- eval
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