Researchers introduced MoME, a Mixture-of-Memory Embeddings method for context-aware sparse lookup that addresses a limitation in existing memory-embedding approaches, which retrieve via a deterministic mechanism. The work targets efficient scaling of large language models by combining sparse capacity mechanisms such as Mixture-of-Experts with token-indexed embedding tables that augment the backbone through cheap parametric lookups.
Scaling large language models efficiently has motivated sparse capacity mechanisms such as Mixture-of-Experts and, more recently, conditional memory: token-indexed embedding tables that augment the backbone with cheap parametric lookups. Existing memory-embedding methods retrieve via a deterministic
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