192 citations · 194 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 2 cited
FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs
Young Jin Kim, Rawn Henry, Raffy Fahim +1
Large Language Models (LLMs) have achieved state-of-the-art performance across various language tasks but pose challenges for practical deployment due to their substantial memory r…
cs.CL2023★ 192 cited
How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation
Amr Hendy, Mohamed Abdelrehim, Amr Sharaf +6
Generative Pre-trained Transformer (GPT) models have shown remarkable capabilities for natural language generation, but their performance for machine translation has not been thoro…
cs.CL2022
Fast Vocabulary Projection Method via Clustering for Multilingual Machine Translation on GPU
Hossam Amer, Young Jin Kim, Mohamed Afify +2
Multilingual Neural Machine Translation has been showing great success using transformer models. Deploying these models is challenging because they usually require large vocabulary…