AI BriefingMemoryLLM: A Plug-and-Play Interpretable Feed-Forward Memory for Transformers
Key point
MemoryLLM proposes separating the FFN from Self-Attention in Transformers and using it as a context-free, token-level neural retrieval memory.
Details
Understanding how Transformers, the core technology behind recent LLMs, operate is critically important. This study addresses the interpretability challenge of the FFN (Feed-Forward Networks) module by proposing MemoryLLM, which separates the FFN from Self-Attention.
MemoryLLM enables the decoupled FFN to be studied as a context-free, token-level neural retrieval memory. Through this, the paper conducts an in-depth investigation into how input tokens access memory locations within the FFN parameters, and how important a role the FFN memory plays across various downstream tasks.
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