6 citations · 12 across the 6 of their papers we have counts for
7 papers
Alignment at Pre-training! Towards Native Alignment for Arabic LLMs
Juhao Liang, Zhenyang Cai, Jianqing Zhu +9
The alignment of large language models (LLMs) is critical for developing effective and safe language models. Traditional approaches focus on aligning models during the instruction…
Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion
Jianqing Zhu, Huang Huang, Zhihang Lin +18
This paper addresses the critical need for democratizing large language models (LLM) in the Arab world, a region that has seen slower progress in developing models comparable to st…
AceGPT, Localizing Large Language Models in Arabic
Huang Huang, Fei Yu, Jianqing Zhu +17
This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addr…
FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting
Jianqing Zhu, Juncai He, Lian Zhang +1
By investigating iterative methods for a constrained linear model, we propose a new class of fully connected V-cycle MgNet for long-term time series forecasting, which is one of th…
An Interpretive Constrained Linear Model for ResNet and MgNet
Juncai He, Jinchao Xu, Lian Zhang +1
We propose a constrained linear data-feature-mapping model as an interpretable mathematical model for image classification using a convolutional neural network (CNN). From this vie…
Integral boundary conditions in phase field models
Xiaofeng Xu, Lian Zhang, Yin Shi +2
Modeling the chemical, electric, and thermal transport as well as phase transitions and the accompanying mesoscale microstructure evolution within a material in an electronic devic…
Make Regularization Effective in Training Sparse CNN
Juncai He, Xiaodong Jia, Jinchao Xu +2
Compressed Sensing using regularization is among the most powerful and popular sparsification technique in many applications, but why has it not been used to obtain sparse…