no code implementations • NAACL 2022 • Hao Zhou, Gongshen Liu, Kewei Tu
Many natural language processing tasks involve text spans and thus high-quality span representations are needed to enhance neural approaches to these tasks.
no code implementations • COLING 2022 • Yichun Zhao, Kui Meng, Gongshen Liu, Jintao Du, Huijia Zhu
Aspect Sentiment Triplet Extraction (ASTE) aims at extracting triplets from a given sentence, where each triplet includes an aspect, its sentiment polarity, and a corresponding opinion explaining the polarity.
no code implementations • CCL 2020 • Wenjie Lu, Leiying Zhou, Gongshen Liu, Quanhai Zhang
At word-level, it calculates semantic similarity between predicted and ground truth words.
no code implementations • 1 Jan 2025 • Jiaxin Song, Xinyu Wang, Yihao Wang, Yifan Tang, Ru Zhang, Jianyi Liu, Gongshen Liu
While manual content moderation is still prevalent, the overwhelming volume of content and the psychological strain on human moderators underscore the need for automated toxic speech detection.
1 code implementation • 3 Dec 2024 • Zongru Wu, Pengzhou Cheng, Lingyong Fang, Zhuosheng Zhang, Gongshen Liu
Backdoor attacks remain significant security threats to generative large language models (LLMs).
1 code implementation • 16 Oct 2024 • Haodong Zhao, Jinming Hu, Peixuan Li, Fangqi Li, Jinrui Sha, Tianjie Ju, Peixuan Chen, Zhuosheng Zhang, Gongshen Liu
Language models (LMs) have emerged as critical intellectual property (IP) assets that necessitate protection.
1 code implementation • 10 Jul 2024 • Tianjie Ju, Yiting Wang, Xinbei Ma, Pengzhou Cheng, Haodong Zhao, Yulong Wang, Lifeng Liu, Jian Xie, Zhuosheng Zhang, Gongshen Liu
The rapid adoption of large language models (LLMs) in multi-agent systems has highlighted their impressive capabilities in various applications, such as collaborative problem-solving and autonomous negotiation.
no code implementations • 22 May 2024 • Pengzhou Cheng, Yidong Ding, Tianjie Ju, Zongru Wu, Wei Du, Ping Yi, Zhuosheng Zhang, Gongshen Liu
To improve the recall of the RAG for the target contexts, we introduce a knowledge graph to construct structured data to achieve hard matching at a fine-grained level.
no code implementations • 7 Mar 2024 • Pengzhou Cheng, Zongru Wu, Gongshen Liu
The STcAM with fine-pruning uses one-dimensional convolution (Conv1D) to extract spatial features and subsequently utilizes the Bidirectional Long Short Term Memory (Bi-LSTM) to extract the temporal features, where the attention mechanism will focus on the important time steps.
no code implementations • 29 Feb 2024 • Pengzhou Cheng, Wei Du, Zongru Wu, Fengwei Zhang, Libo Chen, Gongshen Liu
Specifically, $\mathtt{SynGhost}$ hostilely manipulates clean samples through different syntactic and then maps the backdoor to representation space without disturbing the primitive representation.
1 code implementation • 25 Feb 2024 • Tianjie Ju, Weiwei Sun, Wei Du, Xinwei Yuan, Zhaochun Ren, Gongshen Liu
Previous work has showcased the intriguing capability of large language models (LLMs) in retrieving facts and processing context knowledge.
1 code implementation • 19 Feb 2024 • Tianjie Ju, Yijin Chen, Xinwei Yuan, Zhuosheng Zhang, Wei Du, Yubin Zheng, Gongshen Liu
Recent work has showcased the powerful capability of large language models (LLMs) in recalling knowledge and reasoning.
1 code implementation • 19 Feb 2024 • Zongru Wu, Zhuosheng Zhang, Pengzhou Cheng, Gongshen Liu
In this paper, we investigate the learning mechanisms of backdoor LMs in the frequency space by Fourier analysis.
no code implementations • 15 Feb 2024 • Xinran Chen, Sufeng Duan, Gongshen Liu
Being one of the IR-NAT (Iterative-refinemennt-based NAT) frameworks, the Conditional Masked Language Model (CMLM) adopts the mask-predict paradigm to re-predict the masked low-confidence tokens.
1 code implementation • 18 Jan 2024 • Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, Rui Wang, Gongshen Liu
We introduce R-Judge, a benchmark crafted to evaluate the proficiency of LLMs in judging and identifying safety risks given agent interaction records.
1 code implementation • 20 Nov 2023 • Zhuosheng Zhang, Yao Yao, Aston Zhang, Xiangru Tang, Xinbei Ma, Zhiwei He, Yiming Wang, Mark Gerstein, Rui Wang, Gongshen Liu, Hai Zhao
Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks.
no code implementations • 16 May 2023 • Wei Du, Peixuan Li, Boqun Li, Haodong Zhao, Gongshen Liu
In this paper, we first summarize the requirements that a more threatening backdoor attack against PLMs should satisfy, and then propose a new backdoor attack method called UOR, which breaks the bottleneck of the previous approach by turning manual selection into automatic optimization.
no code implementations • 25 Aug 2022 • Haodong Zhao, Wei Du, Fangqi Li, Peixuan Li, Gongshen Liu
In this paper, we propose "FedPrompt" to study prompt tuning in a model split aggregation way using FL, and prove that split aggregation greatly reduces the communication cost, only 0. 01% of the PLMs' parameters, with little decrease on accuracy both on IID and Non-IID data distribution.
no code implementations • COLING 2022 • Yutao Luo, Menghua Lu, Gongshen Liu, Shilin Wang
To alleviate these problems, we propose a prompt-based approach, Prefix-Controlled Generator (i. e., PCG), for few-shot table-to-text generation.
1 code implementation • 12 Aug 2020 • Hanwen Cao, Yongyi Lu, Cewu Lu, Bo Pang, Gongshen Liu, Alan Yuille
In this paper, we further improve spatio-temporal point cloud feature learning with a flexible module called ASAP considering both attention and structure information across frames, which we find as two important factors for successful segmentation in dynamic point clouds.
no code implementations • ACL 2020 • Jie Zhou, Chunping Ma, Dingkun Long, Guangwei Xu, Ning Ding, Haoyu Zhang, Pengjun Xie, Gongshen Liu
Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy.
General Classification
Hierarchical Multi-label Classification
+3
no code implementations • 18 Feb 2020 • Haolin Zhou, Chaoqi Yang, Xiaofeng Gao, Qiong Chen, Gongshen Liu, Guihai Chen
Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs.
2 code implementations • IJCAI 2019 • Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, Xing Xie
User modeling is an essential task for online rec- ommender systems.
Ranked #2 on
Recommendation Systems
on Amazon Product Data
1 code implementation • EMNLP 2018 • Zuchao Li, Shexia He, Jiaxun Cai, Zhuosheng Zhang, Hai Zhao, Gongshen Liu, Linlin Li, Luo Si
Semantic role labeling (SRL) aims to recognize the predicate-argument structure of a sentence.
no code implementations • ACL 2019 • Jingkang Wang, Jianing Zhou, Jie zhou, Gongshen Liu
Chinese word segmentation (CWS) is often regarded as a character-based sequence labeling task in most current works which have achieved great success with the help of powerful neural networks.
3 code implementations • COLING 2018 • Zeping Yu, Gongshen Liu
In this paper, we introduce sliced recurrent neural networks (SRNNs), which could be parallelized by slicing the sequences into many subsequences.
Ranked #6 on
Sentiment Analysis
on Amazon Review Full
1 code implementation • COLING 2018 • Zhuosheng Zhang, Jiangtong Li, Pengfei Zhu, Hai Zhao, Gongshen Liu
In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation.
Ranked #14 on
Conversational Response Selection
on E-commerce