no code implementations • 23 May 2024 • Pengyue Jia, Yiding Liu, Xiaopeng Li, Xiangyu Zhao, Yuhao Wang, Yantong Du, Xiao Han, Xuetao Wei, Shuaiqiang Wang, Dawei Yin
Worldwide geolocalization aims to locate the precise location at the coordinate level of photos taken anywhere on the Earth.
no code implementations • 8 Mar 2024 • Yiding Liu, Jingjing Wang, Jiamin Luo, Tao Zeng, Guodong Zhou
Specifically, this TSA treats the ACR task as an auxiliary task to boost the performance of the primary ASU task, and further integrates trusted learning into reflexion mechanisms to alleviate the LLMs-intrinsic factual hallucination problem in TSA.
1 code implementation • 23 Feb 2024 • Shenglai Zeng, Jiankun Zhang, Pengfei He, Yue Xing, Yiding Liu, Han Xu, Jie Ren, Shuaiqiang Wang, Dawei Yin, Yi Chang, Jiliang Tang
In this work, we conduct extensive empirical studies with novel attack methods, which demonstrate the vulnerability of RAG systems on leaking the private retrieval database.
no code implementations • 14 Nov 2023 • Yige Zhao, Jianxiang Yu, Yao Cheng, Chengcheng Yu, Yiding Liu, Xiang Li, Shuaiqiang Wang
Instead of directly reconstructing raw features for attributed nodes, SHAVA generates the initial low-dimensional representation matrix for all the nodes, based on which raw features of attributed nodes are further reconstructed to leverage accurate attributes.
no code implementations • 6 Nov 2023 • Zeyuan Zhao, Qingqing Ge, Anfeng Cheng, Yiding Liu, Xiang Li, Shuaiqiang Wang
In addition, most of them only consider the interactions between nodes while neglecting the high-order information behind the latent interactions among different node features.
1 code implementation • 29 Oct 2023 • Pengyue Jia, Yiding Liu, Xiangyu Zhao, Xiaopeng Li, Changying Hao, Shuaiqiang Wang, Dawei Yin
While existing methods expand queries using retrieved or generated contextual documents, each approach has notable limitations.
no code implementations • 26 Oct 2023 • Qingqing Ge, Zeyuan Zhao, Yiding Liu, Anfeng Cheng, Xiang Li, Shuaiqiang Wang, Dawei Yin
Graph Neural Networks (GNNs) are powerful in learning semantics of graph data.
no code implementations • 10 Oct 2023 • Shenglai Zeng, Yaxin Li, Jie Ren, Yiding Liu, Han Xu, Pengfei He, Yue Xing, Shuaiqiang Wang, Jiliang Tang, Dawei Yin
In this work, we conduct the first comprehensive analysis to explore language models' (LMs) memorization during fine-tuning across tasks.
no code implementations • 29 Sep 2023 • Qian Dong, Yiding Liu, Qingyao Ai, Zhijing Wu, Haitao Li, Yiqun Liu, Shuaiqiang Wang, Dawei Yin, Shaoping Ma
Large language models (LLMs) have demonstrated remarkable capabilities across various research domains, including the field of Information Retrieval (IR).
1 code implementation • 4 Jun 2023 • Qian Dong, Yiding Liu, Qingyao Ai, Haitao Li, Shuaiqiang Wang, Yiqun Liu, Dawei Yin, Shaoping Ma
Moreover, the proposed implicit interaction is compatible with special pre-training and knowledge distillation for passage retrieval, which brings a new state-of-the-art performance.
no code implementations • 2 Jun 2023 • Canjia Li, Xiaoyang Wang, Dongdong Li, Yiding Liu, Yu Lu, Shuaiqiang Wang, Zhicong Cheng, Simiu Gu, Dawei Yin
In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner.
no code implementations • 28 Jan 2023 • Anfeng Cheng, Yiding Liu, Weibin Li, Qian Dong, Shuaiqiang Wang, Zhengjie Huang, Shikun Feng, Zhicong Cheng, Dawei Yin
To assess webpage quality from complex DOM tree data, we propose a graph neural network (GNN) based method that extracts rich layout-aware information that implies webpage quality in an end-to-end manner.
1 code implementation • 12 Nov 2022 • Qianru Zhang, Zheng Wang, Cheng Long, Chao Huang, Siu-Ming Yiu, Yiding Liu, Gao Cong, Jieming Shi
Detecting anomalous trajectories has become an important task in many location-based applications.
no code implementations • 18 May 2022 • Yuxiang Lu, Yiding Liu, Jiaxiang Liu, Yunsheng Shi, Zhengjie Huang, Shikun Feng Yu Sun, Hao Tian, Hua Wu, Shuaiqiang Wang, Dawei Yin, Haifeng Wang
Our method 1) introduces a self on-the-fly distillation method that can effectively distill late interaction (i. e., ColBERT) to vanilla dual-encoder, and 2) incorporates a cascade distillation process to further improve the performance with a cross-encoder teacher.
no code implementations • 25 Apr 2022 • Qian Dong, Yiding Liu, Suqi Cheng, Shuaiqiang Wang, Zhicong Cheng, Shuzi Niu, Dawei Yin
To leverage a reliable knowledge, we propose a novel knowledge graph distillation method and obtain a knowledge meta graph as the bridge between query and passage.
no code implementations • 7 Jun 2021 • Yiding Liu, Guan Huang, Jiaxiang Liu, Weixue Lu, Suqi Cheng, Yukun Li, Daiting Shi, Shuaiqiang Wang, Zhicong Cheng, Dawei Yin
More importantly, we present a practical system workflow for deploying the model in web-scale retrieval.
1 code implementation • 28 May 2021 • Siyuan Guo, Lixin Zou, Yiding Liu, Wenwen Ye, Suqi Cheng, Shuaiqiang Wang, Hechang Chen, Dawei Yin, Yi Chang
Based on it, a more robust doubly robust (MRDR) estimator has been proposed to further reduce its variance while retaining its double robustness.
no code implementations • 5 Mar 2020 • Zheng Wang, Cheng Long, Gao Cong, Yiding Liu
Similar trajectory search is a fundamental problem and has been well studied over the past two decades.
1 code implementation • 24 Jan 2019 • Huaxiu Yao, Yiding Liu, Ying WEI, Xianfeng Tang, Zhenhui Li
Specifically, our proposed model is designed as a spatial-temporal network with a meta-learning paradigm.
1 code implementation • ECCV 2018 • Yiding Liu, Siyu Yang, Bin Li, Wengang Zhou, Jizheng Xu, Houqiang Li, Yan Lu
We present an instance segmentation scheme based on pixel affinity information, which is the relationship of two pixels belonging to a same instance.
no code implementations • 12 Apr 2018 • Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay, Yiding Liu, Gao Cong, Xiao-Li Li
Our proposed approach hinges upon the key intuition that the decision making process (in groups) is generally dynamic, i. e., a user's decision is highly dependent on the other group members.