14 citations · 21 across the 7 of their papers we have counts for
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cs.CL2023★ 7 cited
On the Impact of Calibration Data in Post-training Quantization and Pruning
Miles Williams, Nikolaos Aletras
Quantization and pruning form the foundation of compression for neural networks, enabling efficient inference for large language models (LLMs). Recently, various quantization and p…
cs.CL2023★ 14 cited
Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization
George Chrysostomou, Zhixue Zhao, Miles Williams +1
Despite the remarkable performance of generative large language models (LLMs) on abstractive summarization, they face two significant challenges: their considerable size and tenden…
cs.CL2023
Vocabulary-level Memory Efficiency for Language Model Fine-tuning
Miles Williams, Nikolaos Aletras
The extensive memory footprint of language model (LM) fine-tuning poses a challenge for both researchers and practitioners. LMs use an embedding matrix to represent extensive vocab…