46 citations · 56 across the 11 of their papers we have counts for
11 papers
Towards Privacy-Preserving Machine Translation at the Inference Stage: A New Task and Benchmark
Wei Shao, Lemao Liu, Yinqiao Li +3
Current online translation services require sending user text to cloud servers, posing a risk of privacy leakage when the text contains sensitive information. This risk hinders the…
Enhancing Low-Rank Adaptation with Structured Nonlinear Transformations
Guanzhi Deng, Mingyang Liu, Dapeng Wu +2
Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient fine-tuning method for large language models. However, its linear nature limits expressiveness. We propose LoRAN,…
KLIPA: A Knowledge Graph and LLM-Driven QA Framework for IP Analysis
Guanzhi Deng, Yi Xie, Yu-Keung Ng +6
Effectively managing intellectual property is a significant challenge. Traditional methods for patent analysis depend on labor-intensive manual searches and rigid keyword matching.…
NDP: Next Distribution Prediction as a More Broad Target
Junhao Ruan, Abudukeyumu Abudula, Xinyu Liu +7
Large language models (LLMs) trained on next-token prediction (NTP) paradigm have demonstrated powerful capabilities. However, the existing NTP paradigm contains several limitation…
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…
The NiuTrans Machine Translation Systems for WMT21
Shuhan Zhou, Tao Zhou, Binghao Wei +15
This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks. We made submissions to 9 language directions, including English$\leftrighta…
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking
Chi Hu, Chenglong Wang, Xiangnan Ma +5
This paper addresses the efficiency challenge of Neural Architecture Search (NAS) by formulating the task as a ranking problem. Previous methods require numerous training examples…
The NiuTrans System for WNGT 2020 Efficiency Task
Chi Hu, Bei Li, Ye Lin +5
This paper describes the submissions of the NiuTrans Team to the WNGT 2020 Efficiency Shared Task. We focus on the efficient implementation of deep Transformer models \cite{wang-et…
Learning Architectures from an Extended Search Space for Language Modeling
Yinqiao Li, Chi Hu, Yuhao Zhang +6
Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell. In…
Multi-layer Representation Fusion for Neural Machine Translation
Qiang Wang, Fuxue Li, Tong Xiao +3
Neural machine translation systems require a number of stacked layers for deep models. But the prediction depends on the sentence representation of the top-most layer with no acces…
Sharing Attention Weights for Fast Transformer
Tong Xiao, Yinqiao Li, Jingbo Zhu +2
Recently, the Transformer machine translation system has shown strong results by stacking attention layers on both the source and target-language sides. But the inference of this m…