LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining
A new architectural framework accelerating LLM pretraining by 1.33x through specialized locality and memory modules.
Large language models (LLMs) have achieved remarkable breakthroughs across various applications. However, their architectures remain inefficient in pretraining due to two main limitations: (i) self-attention lacks an explicit inductive bias for locality, leading to redundant mode...
ArXiv CS.LG
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LoKiFormer addresses critical inefficiencies in current transformer architectures by decoupling local pattern processing from global knowledge retrieval. This architectural evolution could significantly reduce the immense computational costs associated with pretraining next-generation large language models.
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