1 citations · 2 across the 4 of their papers we have counts for
4 papers
Translate-and-Revise: Boosting Large Language Models for Constrained Translation
Pengcheng Huang, Yongyu Mu, Yuzhang Wu +4
Imposing constraints on machine translation systems presents a challenging issue because these systems are not trained to make use of constraints in generating adequate, fluent tra…
Hybrid Alignment Training for Large Language Models
Chenglong Wang, Hang Zhou, Kaiyan Chang +5
Alignment training is crucial for enabling large language models (LLMs) to cater to human intentions and preferences. It is typically performed based on two stages with different o…
Augmenting Large Language Model Translators via Translation Memories
Yongyu Mu, Abudurexiti Reheman, Zhiquan Cao +6
Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. In this work, we take a step towards prompting large langu…
Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection
Chenglong Wang, Yi Lu, Yongyu Mu +3
Knowledge distillation addresses the problem of transferring knowledge from a teacher model to a student model. In this process, we typically have multiple types of knowledge extra…