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Transformer layers emerge from viewing attention and MLPs as memory stores
The explanation models layers as memory stores, deriving capacity limits for linear retrieval and presenting an MLP as a nonlinear memory store that suppresses weak matches. This offers engineers an intuitive way to reason about interference and transformer components.
- What
- The post explains transformer layers as memory stores, connecting attention and MLPs to key-value recall.
- How
- An MLP applies a nonlinear function to key similarities before combining values, suppressing low-similarity memories.
- Why it matters
- This view helps engineers reason about how model layers store and retrieve information, and how activation choices affect interference.
Read on alexwang.ai → Hacker News · 60 comments
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