no code implementations • Findings (EMNLP) 2021 • Bo Ouyang, Wenbing Huang, Runfa Chen, Zhixing Tan, Yang Liu, Maosong Sun, Jihong Zhu
Knowledge representation learning (KRL) has been used in plenty of knowledge-driven tasks.
no code implementations • EMNLP 2021 • Yuanhang Zheng, Zhixing Tan, Meng Zhang, Mieradilijiang Maimaiti, Huanbo Luan, Maosong Sun, Qun Liu, Yang Liu
Quality estimation (QE) of machine translation (MT) aims to evaluate the quality of machine-translated sentences without references and is important in practical applications of MT.
1 code implementation • 20 Dec 2024 • Qianren Mao, Yangyifei Luo, Jinlong Zhang, Hanwen Hao, Zhilong Cao, Xiaolong Wang, Xiao Guan, Zhenting Huang, Weifeng Jiang, Shuyu Guo, Zhentao Han, Qili Zhang, Siyuan Tao, Yujie Liu, Junnan Liu, Zhixing Tan, Jie Sun, Bo Li, Xudong Liu, Richong Zhang, JianXin Li
As the complexity of RAG systems continues to escalate, we underscore the critical need to identify potential failure points in RAG systems.
1 code implementation • 12 Oct 2024 • Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Rongqiao An, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun
The proposed LLM$\times$MapReduce framework splits the entire document into several chunks for LLMs to read and then aggregates the intermediate answers to produce the final output.
1 code implementation • 18 Feb 2024 • Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun
Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns.
no code implementations • 11 Jan 2024 • Tianyu Cui, Yanling Wang, Chuanpu Fu, Yong Xiao, Sijia Li, Xinhao Deng, Yunpeng Liu, Qinglin Zhang, Ziyi Qiu, Peiyang Li, Zhixing Tan, Junwu Xiong, Xinyu Kong, Zujie Wen, Ke Xu, Qi Li
Based on this, we propose a comprehensive taxonomy, which systematically analyzes potential risks associated with each module of an LLM system and discusses the corresponding mitigation strategies.
no code implementations • 10 May 2023 • Yansong Li, Zhixing Tan, Yang Liu
Based on prompt tuning, we propose Privacy-Preserving Prompt Tuning (RAPT), a framework that provides privacy guarantees for LLM services.
no code implementations • 4 May 2023 • Yuanhang Zheng, Zhixing Tan, Peng Li, Yang Liu
Black-box prompt tuning employs derivative-free optimization algorithms to learn prompts within low-dimensional subspaces rather than back-propagating through the network of Large Language Models (LLMs).
1 code implementation • 23 May 2022 • Shuo Wang, Peng Li, Zhixing Tan, Zhaopeng Tu, Maosong Sun, Yang Liu
In this work, we propose a template-based method that can yield results with high translation quality and match accuracy and the inference speed of our method is comparable with unconstrained NMT models.
no code implementations • 26 Mar 2022 • Sha Yuan, Hanyu Zhao, Shuai Zhao, Jiahong Leng, Yangxiao Liang, Xiaozhi Wang, Jifan Yu, Xin Lv, Zhou Shao, Jiaao He, Yankai Lin, Xu Han, Zhenghao Liu, Ning Ding, Yongming Rao, Yizhao Gao, Liang Zhang, Ming Ding, Cong Fang, Yisen Wang, Mingsheng Long, Jing Zhang, Yinpeng Dong, Tianyu Pang, Peng Cui, Lingxiao Huang, Zheng Liang, HuaWei Shen, HUI ZHANG, Quanshi Zhang, Qingxiu Dong, Zhixing Tan, Mingxuan Wang, Shuo Wang, Long Zhou, Haoran Li, Junwei Bao, Yingwei Pan, Weinan Zhang, Zhou Yu, Rui Yan, Chence Shi, Minghao Xu, Zuobai Zhang, Guoqiang Wang, Xiang Pan, Mengjie Li, Xiaoyu Chu, Zijun Yao, Fangwei Zhu, Shulin Cao, Weicheng Xue, Zixuan Ma, Zhengyan Zhang, Shengding Hu, Yujia Qin, Chaojun Xiao, Zheni Zeng, Ganqu Cui, Weize Chen, Weilin Zhao, Yuan YAO, Peng Li, Wenzhao Zheng, Wenliang Zhao, Ziyi Wang, Borui Zhang, Nanyi Fei, Anwen Hu, Zenan Ling, Haoyang Li, Boxi Cao, Xianpei Han, Weidong Zhan, Baobao Chang, Hao Sun, Jiawen Deng, Chujie Zheng, Juanzi Li, Lei Hou, Xigang Cao, Jidong Zhai, Zhiyuan Liu, Maosong Sun, Jiwen Lu, Zhiwu Lu, Qin Jin, Ruihua Song, Ji-Rong Wen, Zhouchen Lin, LiWei Wang, Hang Su, Jun Zhu, Zhifang Sui, Jiajun Zhang, Yang Liu, Xiaodong He, Minlie Huang, Jian Tang, Jie Tang
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
1 code implementation • ACL 2022 • Shuo Wang, Zhixing Tan, Yang Liu
In this work, we propose to open this black box by directly integrating the constraints into NMT models.
1 code implementation • ACL 2022 • Zhixing Tan, Xiangwen Zhang, Shuo Wang, Yang Liu
Prompting has recently been shown as a promising approach for applying pre-trained language models to perform downstream tasks.
3 code implementations • 29 Jun 2021 • Xiangzhe Kong, Wenbing Huang, Zhixing Tan, Yang Liu
Molecule generation is central to a variety of applications.
no code implementations • 25 Jun 2021 • Shuo Wang, Zhaopeng Tu, Zhixing Tan, Wenxuan Wang, Maosong Sun, Yang Liu
Inspired by the recent progress of large-scale pre-trained language models on machine translation in a limited scenario, we firstly demonstrate that a single language model (LM4MT) can achieve comparable performance with strong encoder-decoder NMT models on standard machine translation benchmarks, using the same training data and similar amount of model parameters.
2 code implementations • 20 Jun 2021 • Zhengyan Zhang, Yuxian Gu, Xu Han, Shengqi Chen, Chaojun Xiao, Zhenbo Sun, Yuan YAO, Fanchao Qi, Jian Guan, Pei Ke, Yanzheng Cai, Guoyang Zeng, Zhixing Tan, Zhiyuan Liu, Minlie Huang, Wentao Han, Yang Liu, Xiaoyan Zhu, Maosong Sun
We present a suite of cost-effective techniques for the use of PLMs to deal with the efficiency issues of pre-training, fine-tuning, and inference.
no code implementations • Findings (ACL) 2021 • Shuo Wang, Zhaopeng Tu, Zhixing Tan, Shuming Shi, Maosong Sun, Yang Liu
Language coverage bias, which indicates the content-dependent differences between sentence pairs originating from the source and target languages, is important for neural machine translation (NMT) because the target-original training data is not well exploited in current practice.
1 code implementation • 14 May 2021 • Zhixing Tan, Zeyuan Yang, Meng Zhang, Qun Liu, Maosong Sun, Yang Liu
With the rapid development of artificial intelligence (AI), there is a trend in moving AI applications, such as neural machine translation (NMT), from cloud to mobile devices.
no code implementations • 31 Dec 2020 • Zhixing Tan, Shuo Wang, Zonghan Yang, Gang Chen, Xuancheng Huang, Maosong Sun, Yang Liu
Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers.
1 code implementation • 14 Jul 2020 • Xuancheng Huang, Jiacheng Zhang, Zhixing Tan, Derek F. Wong, Huanbo Luan, Jingfang Xu, Maosong Sun, Yang Liu
System combination is an important technique for combining the hypotheses of different machine translation systems to improve translation performance.
no code implementations • IJCNLP 2019 • Mingxuan Wang, Jun Xie, Zhixing Tan, Jinsong Su, Deyi Xiong, Lei LI
In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as \textsc{CapsNMT}.
no code implementations • COLING 2018 • Mingxuan Wang, Jun Xie, Zhixing Tan, Jinsong Su, Deyi Xiong, Chao Bian
Neural machine translation with source-side attention have achieved remarkable performance.
1 code implementation • 5 Dec 2017 • Zhixing Tan, Mingxuan Wang, Jun Xie, Yidong Chen, Xiaodong Shi
Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied.
Ranked #15 on
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no code implementations • WS 2017 • Boli Wang, Zhixing Tan, Jinming Hu, Yidong Chen, Xiaodong Shi
This paper describes the Neural Machine Translation systems of Xiamen University for the shared translation tasks of WAT 2017.
no code implementations • IJCNLP 2017 • Boli Wang, Zhixing Tan, Jinming Hu, Yidong Chen, Xiaodong Shi
We demonstrate a neural machine translation web service.
no code implementations • 25 Sep 2016 • Jinsong Su, Zhixing Tan, Deyi Xiong, Rongrong Ji, Xiaodong Shi, Yang Liu
Neural machine translation (NMT) heavily relies on word-level modelling to learn semantic representations of input sentences.