5 citations · 11 across the 4 of their papers we have counts for
4 papers
Unified Demonstration Retriever for In-Context Learning
Xiaonan Li, Kai Lv, Hang Yan +6
In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction.…
Candidate Soups: Fusing Candidate Results Improves Translation Quality for Non-Autoregressive Translation
Huanran Zheng, Wei Zhu, Pengfei Wang +1
Non-autoregressive translation (NAT) model achieves a much faster inference speed than the autoregressive translation (AT) model because it can simultaneously predict all tokens du…
AutoRC: Improving BERT Based Relation Classification Models via Architecture Search
Wei Zhu, Xipeng Qiu, Yuan Ni +1
Although BERT based relation classification (RC) models have achieved significant improvements over the traditional deep learning models, it seems that no consensus can be reached…
AutoTrans: Automating Transformer Design via Reinforced Architecture Search
Wei Zhu, Xiaoling Wang, Xipeng Qiu +2
Though the transformer architectures have shown dominance in many natural language understanding tasks, there are still unsolved issues for the training of transformer models, espe…