1 code implementation • 31 Oct 2023 • Kaixin Li, Qisheng Hu, Xu Zhao, Hui Chen, Yuxi Xie, Tiedong Liu, Qizhe Xie, Junxian He
In this work, we explore the use of Large Language Models (LLMs) to edit code based on user instructions.
2 code implementations • 3 Aug 2023 • Keyu Duan, Qian Liu, Tat-Seng Chua, Shuicheng Yan, Wei Tsang Ooi, Qizhe Xie, Junxian He
More recently, with the rapid development of language models (LMs), researchers have focused on leveraging LMs to facilitate the learning of TGs, either by jointly training them in a computationally intensive framework (merging the two stages), or designing complex self-supervised training tasks for feature extraction (enhancing the first stage).
Ranked #1 on Node Property Prediction on ogbn-arxiv
1 code implementation • 11 Jun 2023 • Hai Ye, Qizhe Xie, Hwee Tou Ng
In this work, we study multi-source test-time model adaptation from user feedback, where K distinct models are established for adaptation.
1 code implementation • Github 2023 • Qisheng Hu*, Kaixin Li*, Xu Zhao, Yuxi Xie, Tiedong Liu, Hui Chen, Qizhe Xie, Junxian He
In this work, we explore the use of large language models (LLMs) to edit code based on user instructions, covering a broad range of implicit tasks such as comment insertion, code optimization, and code refactoring.
no code implementations • NeurIPS 2023 • Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, Xu Zhao, Min-Yen Kan, Junxian He, Qizhe Xie
Stochastic beam search balances exploitation and exploration of the search space with temperature-controlled randomness.
9 code implementations • CVPR 2021 • Hieu Pham, Zihang Dai, Qizhe Xie, Minh-Thang Luong, Quoc V. Le
We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90. 2% on ImageNet, which is 1. 6% better than the existing state-of-the-art.
13 code implementations • CVPR 2020 • Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le
During the learning of the student, we inject noise such as dropout, stochastic depth, and data augmentation via RandAugment to the student so that the student generalizes better than the teacher.
Ranked #16 on Image Classification on ImageNet ReaL (using extra training data)
20 code implementations • NeurIPS 2020 • Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, Quoc V. Le
In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning.
Ranked #1 on Sentiment Analysis on Amazon Review Full
no code implementations • 1 Oct 2018 • Filip Ilievski, Eduard Hovy, Qizhe Xie, Piek Vossen
The human mind is a powerful multifunctional knowledge storage and management system that performs generalization, type inference, anomaly detection, stereotyping, and other tasks.
1 code implementation • 25 Sep 2018 • Xiang Kong, Qizhe Xie, Zihang Dai, Eduard Hovy
Mixture of Softmaxes (MoS) has been shown to be effective at addressing the expressiveness limitation of Softmax-based models.
Ranked #18 on Machine Translation on WMT2014 English-French
1 code implementation • ACL 2018 • Zihang Dai, Qizhe Xie, Eduard Hovy
In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning.
no code implementations • ICLR 2018 • Qizhe Xie, Guokun Lai, Zihang Dai, Eduard Hovy
Cloze test is widely adopted in language exams to evaluate students' language proficiency.
no code implementations • EMNLP 2018 • Qizhe Xie, Guokun Lai, Zihang Dai, Eduard Hovy
Cloze tests are widely adopted in language exams to evaluate students' language proficiency.
no code implementations • NeurIPS 2017 • Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, Graham Neubig
Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning.
no code implementations • ACL 2017 • Qizhe Xie, Xuezhe Ma, Zihang Dai, Eduard Hovy
Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness.
2 code implementations • EMNLP 2017 • Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, Eduard Hovy
We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task.
no code implementations • 14 Jul 2015 • Kai Sun, Qizhe Xie, Kai Yu
Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses.