1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024
A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models
Pengxiang Zhao, Hanyu Hu, Ping Li +3
Pruning is a critical strategy for compressing trained large language models (LLMs), aiming at substantial memory conservation and computational acceleration without compromising p…
cs.CL2023★ 1 cited
How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions
Houquan Zhou, Yang Hou, Zhenghua Li +4
While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or…
cs.CL2023
AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing
Asaad Alghamdi, Xinyu Duan, Wei Jiang +9
Developing monolingual large Pre-trained Language Models (PLMs) is shown to be very successful in handling different tasks in Natural Language Processing (NLP). In this work, we pr…