20 citations · 102 across the 20 of their papers we have counts for
6 papers · 1 filter
MERIT: Maximum-normalized Element-wise Ratio for Language Model Large-batch Training
Yang Luo, Zangwei Zheng, Ziheng Qin +3
Large-batch training has become a cornerstone in accelerating the training of deep neural networks, yet it poses challenges in optimization and generalization. Existing optimizers…
Dataset Growth
Ziheng Qin, Zhaopan Xu, Yukun Zhou +10
Deep learning benefits from the growing abundance of available data. Meanwhile, efficiently dealing with the growing data scale has become a challenge. Data publicly available are…
To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis
Fuzhao Xue, Yao Fu, Wangchunshu Zhou +2
Recent research has highlighted the importance of dataset size in scaling language models. However, large language models (LLMs) are notoriously token-hungry during pre-training, a…
A Study on Transformer Configuration and Training Objective
Fuzhao Xue, Jianghai Chen, Aixin Sun +6
Transformer-based models have delivered impressive results on many tasks, particularly vision and language tasks. In many model training situations, conventional configurations are…
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU
Zangwei Zheng, Pengtai Xu, Xuan Zou +12
The click-through rate (CTR) prediction task is to predict whether a user will click on the recommended item. As mind-boggling amounts of data are produced online daily, accelerati…
Cross-token Modeling with Conditional Computation
Yuxuan Lou, Fuzhao Xue, Zangwei Zheng +1
Mixture-of-Experts (MoE), a conditional computation architecture, achieved promising performance by scaling local module (i.e. feed-forward network) of transformer. However, scalin…