10 citations · 23 across the 9 of their papers we have counts for
9 papers
Improving Translation Faithfulness of Large Language Models via Augmenting Instructions
Yijie Chen, Yijin Liu, Fandong Meng +3
Large Language Models (LLMs) present strong general capabilities, and a current compelling challenge is stimulating their specialized capabilities, such as machine translation, thr…
Instruction Position Matters in Sequence Generation with Large Language Models
Yijin Liu, Xianfeng Zeng, Fandong Meng +1
Large language models (LLMs) are capable of performing conditional sequence generation tasks, such as translation or summarization, through instruction fine-tuning. The fine-tuning…
Towards Multiple References Era -- Addressing Data Leakage and Limited Reference Diversity in NLG Evaluation
Xianfeng Zeng, Yijin Liu, Fandong Meng +1
N-gram matching-based evaluation metrics, such as BLEU and chrF, are widely utilized across a range of natural language generation (NLG) tasks. However, recent studies have reveale…
BranchNorm: Robustly Scaling Extremely Deep Transformers
Yijin Liu, Xianfeng Zeng, Fandong Meng +1
Recently, DeepNorm scales Transformers into extremely deep (i.e., 1000 layers) and reveals the promising potential of deep scaling. To stabilize the training of deep models, DeepNo…
Towards Robust Online Dialogue Response Generation
Leyang Cui, Fandong Meng, Yijin Liu +2
Although pre-trained sequence-to-sequence models have achieved great success in dialogue response generation, chatbots still suffer from generating inconsistent responses in real-w…
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation
Songming Zhang, Yijin Liu, Fandong Meng +4
Token-level adaptive training approaches can alleviate the token imbalance problem and thus improve neural machine translation, through re-weighting the losses of different target…