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检索条件"机构=Key Lab of Network Data Science and Technology"
2790 条 记 录,以下是201-210 订阅
排序:
CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval
arXiv
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arXiv 2023年
作者: Cai, Yinqiong Fan, Yixing Bi, Keping Guo, Jiafeng Chen, Wei Zhang, Ruqing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China
The first-stage retrieval aims to retrieve a subset of candidate documents from a huge collection both effectively and efficiently. Since various matching patterns can exist between queries and relevant documents, pre... 详细信息
来源: 评论
Multi-granular Adversarial Attacks against Black-box Neural Ranking Models
arXiv
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arXiv 2024年
作者: Liu, Yu-An Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China University of Amsterdam Amsterdam Netherlands
Adversarial ranking attacks have gained increasing attention due to their success in probing vulnerabilities, and, hence, enhancing the robustness, of neural ranking models. Conventional attack methods employ perturba... 详细信息
来源: 评论
Inducing Causal Structure for Abstractive Text Summarization
arXiv
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arXiv 2023年
作者: Chen, Lu Zhang, Ruqing Huang, Wei Chen, Wei Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China
The mainstream of data-driven abstractive summarization models tends to explore the correlations rather than the causal relationships. Among such correlations, there can be spurious ones which suffer from the language... 详细信息
来源: 评论
L2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible Representations
arXiv
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arXiv 2023年
作者: Cai, Yinqiong Bi, Keping Fan, Yixing Guo, Jiafeng Chen, Wei Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China
First-stage retrieval is a critical task that aims to retrieve relevant document candidates from a large-scale collection. While existing retrieval models have achieved impressive performance, they are mostly studied ... 详细信息
来源: 评论
A Comparative Study of Training Objectives for Clarification Facet Generation
arXiv
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arXiv 2023年
作者: Ni, Shiyu Bi, Keping Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China
Due to the ambiguity and vagueness of a user query, it is essential to identify the query facets for the clarification of user intents. Existing work on query facet generation has achieved compelling performance by se... 详细信息
来源: 评论
Are Large Language Models Good at Utility Judgments?
arXiv
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arXiv 2024年
作者: Zhang, Hengran Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China University of Amsterdam Amsterdam Netherlands
Retrieval-augmented generation (RAG) is considered to be a promising approach to alleviate the hallucination issue of large language models (LLMs), and it has received widespread attention from researchers recently. D... 详细信息
来源: 评论
Ensemble Ranking Model with Multiple Pretraining Strategies for Web Search
arXiv
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arXiv 2023年
作者: Sun, Xiaojie Yu, Lulu Wang, Yiting Bi, Keping Guo, Jiafeng CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China
An effective ranking model usually requires a large amount of training data to learn the relevance between documents and queries. User clicks are often used as training data since they can indicate relevance and are c... 详细信息
来源: 评论
On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective
arXiv
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arXiv 2023年
作者: Liu, Yu-An Zhang, Ruqing Guo, Jiafeng Chen, Wei Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China
Recently, we have witnessed generative retrieval increasingly gaining attention in the information retrieval (IR) field, which retrieves documents by directly generating their identifiers. So far, much effort has been... 详细信息
来源: 评论
Feature-Enhanced network with Hybrid Debiasing Strategies for Unbiased Learning to Rank
arXiv
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arXiv 2023年
作者: Yu, Lulu Wang, Yiting Sun, Xiaojie Bi, Keping Guo, Jiafeng CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China
Unbiased learning to rank (ULTR) aims to mitigate various biases existing in user clicks, such as position bias, trust bias, presentation bias, and learn an effective ranker. In this paper, we introduce our winning ap... 详细信息
来源: 评论
Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM  14th
Heavy Rainfall Prediction Model Using Sample Entropy Derived...
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14th China Satellite Navigation Conference, CSNC 2024
作者: Wu, Fanming Li, Dengao Zhao, Jinhua Feng, Ran Shi, Danyang Zhang, Xinfang Zhao, Jumin College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China
There is a growing interest to use Global Navigation Satellite System (GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and un... 详细信息
来源: 评论