Search Results for author: Quan Du

Found 8 papers, 4 papers with code

RoVRM: A Robust Visual Reward Model Optimized via Auxiliary Textual Preference Data

1 code implementation22 Aug 2024 Chenglong Wang, Yang Gan, Yifu Huo, Yongyu Mu, Murun Yang, Qiaozhi He, Tong Xiao, Chunliang Zhang, Tongran Liu, Quan Du, Di Yang, Jingbo Zhu

However, these techniques face the difficulty arising from the scarcity of visual preference data, which is required to train a visual reward model (VRM).

Hallucination

Learning Evaluation Models from Large Language Models for Sequence Generation

no code implementations8 Aug 2023 Chenglong Wang, Hang Zhou, Kaiyan Chang, Tongran Liu, Chunliang Zhang, Quan Du, Tong Xiao, Jingbo Zhu

Large language models achieve state-of-the-art performance on sequence generation evaluation, but typically have a large number of parameters.

Machine Translation Style Transfer +1

Learning Light-Weight Translation Models from Deep Transformer

1 code implementation27 Dec 2020 Bei Li, Ziyang Wang, Hui Liu, Quan Du, Tong Xiao, Chunliang Zhang, Jingbo Zhu

We proposed a novel group-permutation based knowledge distillation approach to compressing the deep Transformer model into a shallow model.

Knowledge Distillation Machine Translation +2

A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction

no code implementations COLING 2020 Yanyang Li, Yingfeng Luo, Ye Lin, Quan Du, Huizhen Wang, ShuJian Huang, Tong Xiao, Jingbo Zhu

Our experiments show that this simple method does not hamper the performance of similar language pairs and achieves an accuracy of 13. 64~55. 53% between English and four distant languages, i. e., Chinese, Japanese, Vietnamese and Thai.

Dimensionality Reduction Self-Learning

Shallow-to-Deep Training for Neural Machine Translation

1 code implementation EMNLP 2020 Bei Li, Ziyang Wang, Hui Liu, Yufan Jiang, Quan Du, Tong Xiao, Huizhen Wang, Jingbo Zhu

We find that stacking layers is helpful in improving the representation ability of NMT models and adjacent layers perform similarly.

Machine Translation NMT +2

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