AIBullisharXiv – CS AI · 14h ago7/10
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AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM
Researchers introduce AtlasKV, a parametric knowledge integration method that enables large language models to leverage billion-scale knowledge graphs while consuming less than 20GB of VRAM. Unlike traditional retrieval-augmented generation (RAG) approaches, AtlasKV integrates knowledge directly into LLM parameters without requiring external retrievers or extended context windows, reducing inference latency and computational overhead.