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cs.CL2024
Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA
Sangmin Bae, Adam Fisch, Hrayr Harutyunyan +3
Large language models (LLMs) are expensive to deploy. Parameter sharing offers a possible path towards reducing their size and cost, but its effectiveness in modern LLMs remains fa…
cs.CL2024★ 1 cited
Block Transformer: Global-to-Local Language Modeling for Fast Inference
Namgyu Ho, Sangmin Bae, Taehyeon Kim +6
We introduce the Block Transformer which adopts hierarchical global-to-local modeling to autoregressive transformers to mitigate the inference bottlenecks associated with self-atte…