no code implementations • EMNLP 2021 • Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, Kai-Wei Chang
Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models.
no code implementations • Findings (ACL) 2022 • Jianhan Xu, Cenyuan Zhang, Xiaoqing Zheng, Linyang Li, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang
Most of the existing defense methods improve the adversarial robustness by making the models adapt to the training set augmented with some adversarial examples.
no code implementations • EMNLP 2020 • Lihao Wang, Xiaoqing Zheng
A sequence-to-sequence (seq2seq) learning with neural networks empirically shows to be an effective framework for grammatical error correction (GEC), which takes a sentence with errors as input and outputs the corrected one.
no code implementations • 23 May 2024 • Changze Lv, Dongqi Han, Yansen Wang, Xiaoqing Zheng, Xuanjing Huang, Dongsheng Li
Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks that are both energy-efficient and biologically plausible.
no code implementations • 17 Mar 2024 • Cenyuan Zhang, Xiaoqing Zheng, Ruicheng Yin, Shujie Geng, Jianhan Xu, Xuan Gao, Changze Lv, Zixuan Ling, Xuanjing Huang, Miao Cao, Jianfeng Feng
Deciphering natural language from brain activity through non-invasive devices remains a formidable challenge.
no code implementations • 23 Feb 2024 • Muling Wu, Wenhao Liu, Xiaohua Wang, Tianlong Li, Changze Lv, Zixuan Ling, Jianhao Zhu, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
Parameter Efficient Fine-Tuning (PEFT) techniques have drawn significant attention due to their ability to yield competitive results while updating only a small portion of the adjustable parameters.
no code implementations • 2 Feb 2024 • Changze Lv, Yansen Wang, Dongqi Han, Xiaoqing Zheng, Xuanjing Huang, Dongsheng Li
In this paper, we propose a framework for SNNs in time-series forecasting tasks, leveraging the efficiency of spiking neurons in processing temporal information.
no code implementations • 12 Jan 2024 • Tianlong Li, Shihan Dou, Wenhao Liu, Muling Wu, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
To overcome these limitations, we propose a novel jailbreaking approach, named Jailbreaking LLMs through Representation Engineering (JRE).
no code implementations • 26 Dec 2023 • Wenhao Liu, Xiaohua Wang, Muling Wu, Tianlong Li, Changze Lv, Zixuan Ling, Jianhao Zhu, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness.
no code implementations • 25 Oct 2023 • Tianlong Li, Shihan Dou, Changze Lv, Wenhao Liu, Jianhan Xu, Muling Wu, Zixuan Ling, Xiaoqing Zheng, Xuanjing Huang
Users can utilize UBPL to adjust the probability vectors of predicted words in the decoding phase of LLMs, thus influencing the personality expression of LLMs.
1 code implementation • 20 Oct 2023 • Yufei Gu, Xiaoqing Zheng, Tomaso Aste
Double descent presents a counter-intuitive aspect within the machine learning domain, and researchers have observed its manifestation in various models and tasks.
no code implementations • 10 Oct 2023 • Tianlong Li, Wenhao Liu, Changze Lv, Jianhan Xu, Cenyuan Zhang, Muling Wu, Xiaoqing Zheng, Xuanjing Huang
Spiking neural networks (SNNs) have demonstrated the capability to achieve comparable performance to deep neural networks (DNNs) in both visual and linguistic domains while offering the advantages of improved energy efficiency and adherence to biological plausibility.
1 code implementation • 29 Aug 2023 • Changze Lv, Tianlong Li, Jianhan Xu, Chenxi Gu, Zixuan Ling, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
Spiking neural networks (SNNs) offer a promising avenue to implement deep neural networks in a more energy-efficient way.
1 code implementation • 20 Jul 2023 • Shaowu Peng, Pengcheng Zhao, Yongyu Ye, Junying Chen, Yunbing Chang, Xiaoqing Zheng
Endoscopic surgery is currently an important treatment method in the field of spinal surgery and avoiding damage to the spinal nerves through video guidance is a key challenge.
1 code implementation • 24 May 2023 • Hua Cai, Xuli Shen, Qing Xu, Weilin Shen, Xiaomei Wang, Weifeng Ge, Xiaoqing Zheng, xiangyang xue
To this end, we propose a novel approach for empathetic response generation, which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker's situation.
no code implementations • 14 Oct 2022 • Chenxi Gu, Chengsong Huang, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh
Large pre-trained language models (PLMs) have proven to be a crucial component of modern natural language processing systems.
1 code implementation • Findings (ACL) 2022 • Cenyuan Zhang, Xiang Zhou, Yixin Wan, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh
Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool NLP models.
1 code implementation • EMNLP 2021 • Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, Cho-Jui Hsieh
Recent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models.
1 code implementation • ACL 2021 • Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang
Although deep neural networks have achieved prominent performance on many NLP tasks, they are vulnerable to adversarial examples.
1 code implementation • ACL 2021 • Chong Li, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spelling errors as input and outputs the corrected one.
1 code implementation • 8 May 2021 • Jiehang Zeng, Xiaoqing Zheng, Jianhan Xu, Linyang Li, Liping Yuan, Xuanjing Huang
Recently, few certified defense methods have been developed to provably guarantee the robustness of a text classifier to adversarial synonym substitutions.
no code implementations • 22 Mar 2021 • Liping Yuan, Jiehang Zeng, Xiaoqing Zheng
It is still a challenging task to learn a neural text generation model under the framework of generative adversarial networks (GANs) since the entire training process is not differentiable.
no code implementations • 22 Mar 2021 • Liping Yuan, Jiangtao Feng, Xiaoqing Zheng, Xuanjing Huang
The key idea is that at each time step, the network takes as input a ``bundle'' of similar words predicted at the previous step instead of a single ground truth.
1 code implementation • ACL 2021 • Tao Gui, Xiao Wang, Qi Zhang, Qin Liu, Yicheng Zou, Xin Zhou, Rui Zheng, Chong Zhang, Qinzhuo Wu, Jiacheng Ye, Zexiong Pang, Yongxin Zhang, Zhengyan Li, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Bolin Zhu, Shan Qin, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one.
1 code implementation • 2 Mar 2021 • Lihao Wang, Zongyi Li, Xiaoqing Zheng
We present an unsupervised word segmentation model, in which the learning objective is to maximize the generation probability of a sentence given its all possible segmentation.
no code implementations • 17 Nov 2020 • Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, Kai-Wei Chang
Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Lu Liu, Yi Zhou, Jianhan Xu, Xiaoqing Zheng, Kai-Wei Chang, Xuanjing Huang
The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM).
no code implementations • 19 Sep 2020 • Xiaoqing Zheng, Jie Chen, Hongcheng Wang, Song Zheng, Yaguang Kong
A machine vision-based surface quality inspection system is usually composed of two processes: image acquisition and automatic defect detection.
no code implementations • 16 Sep 2020 • Zongyi Li, Xiaoqing Zheng, Jun He
We present RepRank, an unsupervised graph-based ranking model for extractive multi-document summarization in which the similarity between words, sentences, and word-to-sentence can be estimated by the distances between their vector representations in a unified vector space.
no code implementations • 10 Sep 2020 • Lu Liu, Zhenqiao Song, Xiaoqing Zheng, Jun He
One of the major challenges in coreference resolution is how to make use of entity-level features defined over clusters of mentions rather than mention pairs.
no code implementations • ACL 2020 • Xiaoqing Zheng, Jiehang Zeng, Yi Zhou, Cho-Jui Hsieh, Minhao Cheng, Xuanjing Huang
Despite achieving prominent performance on many important tasks, it has been reported that neural networks are vulnerable to adversarial examples.
1 code implementation • 20 Jun 2020 • Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang
Despite neural networks have achieved prominent performance on many natural language processing (NLP) tasks, they are vulnerable to adversarial examples.
no code implementations • 15 Apr 2020 • Jiehang Zeng, Lu Liu, Xiaoqing Zheng
A generative network (GN) takes two elements of a (subject, predicate, object) triple as input and generates the vector representation of the missing element.
1 code implementation • 17 Dec 2019 • Yi Zhou, Xiaoqing Zheng, Xuanjing Huang
Inspired by a concept of content-addressable retrieval from cognitive science, we propose a novel fragment-based model augmented with a lexicon-based memory for Chinese NER, in which both the character-level and word-level features are combined to generate better feature representations for possible name candidates.
Chinese Named Entity Recognition named-entity-recognition +4
no code implementations • ACL 2019 • Zhenqiao Song, Xiaoqing Zheng, Lu Liu, Mu Xu, Xuanjing Huang
It is desirable for dialog systems to have capability to express specific emotions during a conversation, which has a direct, quantifiable impact on improvement of their usability and user satisfaction.
no code implementations • EMNLP 2017 • Xiaoqing Zheng
Very recently, some studies on neural dependency parsers have shown advantage over the traditional ones on a wide variety of languages.