This research paper explores the effectiveness of memory layers in significantly enhancing large language models (LLMs). By incorporating a trainable key-value lookup mechanism, memory layers add parameters without increasing computational cost, improving factual accuracy and overall performance on various tasks. The researchers demonstrate substantial gains, especially on factual tasks, even surpassing models with much larger computational budgets and outperforming mixture-of-experts models. They detail improvements in memory layer implementation, achieving scalability with up to 128 billion memory parameters, and discuss various architectural optimizations. The findings strongly advocate for integrating memory layers into future AI architectures.

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