MoME: Mixture-of-Memory Embeddings for Context-Aware Sparse Lookup

2026-09-14

AI Summary

为解决上下文感知稀疏查找问题，提出Mixture-of-Memory Embeddings（MoME）方法，通过混合多个记忆槽并根据隐藏状态选择读取槽来提高模型性能。

Abstract

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 function of the surface form, which collapses different contextual senses of the same token (e.g., python the language vs. the animal) into a single fixed entry. We introduce Mixture of Memory Embeddings (MoME), a context-aware memory mechanism that replaces each token's single memory row with a mixture of M slots and uses a learned gate over the hidden state to choose which slots to read at each position. In controlled pretraining experiments across nanochat, Llama-3/MobileLLM, and Qwen3 backbones, MoME improves over Value Embedding, Bigram, and STEM baselines in iso-parameter and iso-training-FLOP settings, shows a more promising memory-size scaling trend at sub-billion scale, and remains efficient in training and inference. Qualitative routing analyses on polysemous tokens further suggest that the learned mixture exhibits a degree of semantic interpretability, dispatching the same surface token to distinct memory slots under different senses.

Problem

• context-aware
• memory embedding
• sparse lookup
• polysemous tokens
• Mixture-of-Experts

Innovation

• Mixture-of-Memory Embeddings
• context-aware memory
• learned gate
• semantic interpretability