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cs.CL2024
Distributed In-Context Learning under Non-IID Among Clients
Siqi Liang, Sumyeong Ahn, Jiayu Zhou
Advancements in large language models (LLMs) have shown their effectiveness in multiple complicated natural language reasoning tasks. A key challenge remains in adapting these mode…
cs.CL2023
NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models
Jongwoo Ko, Seungjoon Park, Yujin Kim +4
Structured pruning methods have proven effective in reducing the model size and accelerating inference speed in various network architectures such as Transformers. Despite the vers…