20 citations · 45 across the 12 of their papers we have counts for
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cs.CL2023★ 3 cited
Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer
Boan Liu, Liang Ding, Li Shen +4
The Mixture of Experts (MoE) has emerged as a highly successful technique in deep learning, based on the principle of divide-and-conquer to maximize model capacity without signific…
cs.CL2023★ 20 cited
Towards Making the Most of ChatGPT for Machine Translation
Keqin Peng, Liang Ding, Qihuang Zhong +5
ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource lan…
cs.CL2023★ 10 cited
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE
Qihuang Zhong, Liang Ding, Keqin Peng +5
This technical report briefly describes our JDExplore d-team's submission Vega v1 on the General Language Understanding Evaluation (GLUE) leaderboard, where GLUE is a collection of…