EngramEdit: Decoupled Knowledge Updates in LLMs through Conditional Memory
Conditional memory architectures such as DeepSeek Engram use input n-grams to look up learned embeddings, expanding the capacity of large language models (LLMs) with limited additional computation. Beyond model scaling, this architecture has demonstrated the potential to decouple factual knowledge storage from general-purpose computation, offering a promising route to updating
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Conditional memory architectures such as DeepSeek Engram use input n-grams to look up learned embeddings, expanding the capacity of large language models (LLMs) with limited additional computation. Beyond model scaling, this architecture has demonstrated the potential to decouple factual knowledge storage from general-purpose computation, offering a promising route to updating
This paper introduces a method for decoupled knowledge updates in LLMs, which is crucial for maintaining factual accuracy and reducing the cost of retraining. As LLMs become more integrated into real-world applications, the ability to efficiently update specific knowledge without affecting general capabilities will be a key differentiator for ML engineers.