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cs.CL2024
FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing
Zekai Li, Jintu Zheng, Ji Liu +9
Recently, large language models (LLMs) have demonstrated superior performance across various tasks by adhering to scaling laws, which significantly increase model size. However, th…
cs.CL2024
Enhancing One-shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism
Guanchen Li, Xiandong Zhao, Lian Liu +6
Pre-trained language models (PLMs) are engineered to be robust in contextual understanding and exhibit outstanding performance in various natural language processing tasks. However…
cs.AI2024
Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference
Zeping Li, Xinlong Yang, Ziheng Gao +7
Large Language Models (LLMs) inherently use autoregressive decoding, which lacks parallelism in inference and results in significantly slow inference speed. While methods such as M…