检索结果-内蒙古大学图书馆

LLM4RSR: Large Language Models as Data Correctors for Robust Sequential Recommendation 39

学校读者我要写书评

暂无评论

LLM4RSR: Large Language Models as Data Correctors for Robust...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Yan Wang, Bin Qu, Xinghua School of Computer Science and Engineering Northeastern University China School of Computing Macquarie University Australia Singapore University of Technology and Design Singapore National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Ministry of Education China Singapore

ISBN: (纸本)157735897X

Sequential Recommenders (SRs) are trained to predict the next item as the target given its preceding items as the input, assuming every input-target pair is matched and is reliable for training. However, users can be induced by external distractions to click on items inconsistent with their true preferences, resulting in unreliable training instances with mismatched input-target pairs. To resist unreliable data, researchers attempt to develop Robust SRs (RSRs). However, our data analysis unveils that existing RSRs are data-driven. That is, for most instances formed by infrequently co-occurred items, existing RSRs are uncertain about their reliability. To fill this gap, we propose a generic framework - LLM4RSR (Large Language Models for Robust Sequential Recommendation) to semantically complement data-driven RSRs by correcting uncertain instances into reliable ones based on LLMs' semantic comprehension of items beyond co-occurrence. In this way, RSRs can be re-trained with the corrected data for better accuracy. This is a selective knowledge distillation procedure, where the LLM acts as a teacher guiding student RSRs via uncertain instances. To align LLMs with the data correction task and mitigate inherent hallucinations, we equip the LLM with profile, plan, and memory modules, which are automatically optimized via textual gradient descent, eliminating the need for human effort and expertise. Experiments on four real-world datasets spanning eight backbones verify the generality, effectiveness, and efficiency of LLM4RSR. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Recommender systems

OCDB: REVISITING CAUSAL DISCOVERY WITH A COMPREHENSIVE BENCHMARK AND EVALUATION FRAMEWORK

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhou, Wei Huang, Hong Zhang, Guowen Shi, Ruize Yin, Kehan Lin, Yuanyuan Liu, Bang Huazhong University of Science and Technology China DIRO Université de Montréal & Mila Canada CIFAR AI Chair Canada The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Large language models (LLMs) have excelled in various natural language processing tasks, but challenges in interpretability and trustworthiness persist, limiting their use in high-stakes fields. Causal discovery offers a promising approach to improve transparency and reliability. However, current evaluations are often one-sided and lack assessments focused on interpretability performance. Additionally, these evaluations rely on synthetic data and lack comprehensive assessments of real-world datasets. These lead to promising methods potentially being overlooked. To address these issues, we propose a flexible evaluation framework with metrics for evaluating differences in causal structures and causal effects, which are crucial attributes that help improve the interpretability of LLMs. We introduce the Open Causal Discovery Benchmark (OCDB), based on real data, to promote fair comparisons and drive optimization of algorithms. Additionally, our new metrics account for undirected edges, enabling fair comparisons between Directed Acyclic Graphs (DAGs) and Completed Partially Directed Acyclic Graphs (CPDAGs). Experimental results show significant shortcomings in existing algorithms' generalization capabilities on real data, highlighting the potential for performance improvement and the importance of our framework in advancing causal discovery techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Directed graphs

YOLOO: You Only Learn from Others Once

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Gu, Lipeng Wei, Mingqiang Yan, Xuefeng Zhu, Dingkun Zhao, Wei Xie, Haoran Liu, Yong-Jin School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Shenzhen Institute of Research Nanjing University of Aeronautics and Astronautics Shenzhen China The School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China The School of Computer Science Jiangsu University of Technology Changzhou China The Division of Artificial Intelligence School of Data Science Lingnan University New Territories Hong Kong The MOE-Key Laboratory of Pervasive Computing Department of Computer Science and Technology Tsinghua University Beijing China

Multi-modal 3D multi-object tracking (MOT) typically necessitates extensive computational costs of deep neural networks (DNNs) to extract multi-modal representations. In this paper, we propose an intriguing question: May we learn from multiple modalities only during training to avoid multi-modal input in the inference phase? To answer it, we propose YOLOO, a novel multi-modal 3D MOT paradigm: You Only Learn from Others Once. YOLOO empowers the point cloud encoder to learn a unified tri-modal representation (UTR) from point clouds and other modalities, such as images and textual cues, all at once. Leveraging this UTR, YOLOO achieves efficient tracking solely using the point cloud encoder without compromising its performance, fundamentally obviating the need for computationally intensive DNNs. Specifically, YOLOO includes two core components: a unified tri-modal encoder (UTEnc) and a flexible geometric constraint (F-GC) module. UTEnc integrates a point cloud encoder with image and text encoders adapted from pretrained CLIP. It seamlessly fuses point cloud information with rich visual-textual knowledge from CLIP into the point cloud encoder, yielding highly discriminative UTRs that facilitate the association between trajectories and detections. Additionally, FGC filters out mismatched associations with similar representations but significant positional discrepancies. It further enhances the robustness of UTRs without requiring any scene-specific tuning, addressing a key limitation of customized geometric constraints (e.g., 3D IoU). Lastly, high-quality 3D trajectories are generated by a traditional data association component. By integrating these advancements into a multi-modal 3D MOT scheme, our YOLOO achieves substantial gains in both robustness and efficiency. Comprehensive experiments on KITTI and Waymo demonstrate the superior performance of our YOLOO over its twenty competitors. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

Quantum Algorithms and Lower Bounds for Finite-Sum Optimization

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhang, Yexin Zhang, Chenyi Fang, Cong Wang, Liwei Li, Tongyang School of Electronics Engineering and Computer Science Peking University China Computer Science Department Stanford University United States National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Center on Frontiers of Computing Studies Peking University China School of Computer Science Peking University China

Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, etc. In this paper, we initiate the study of solving finite-sum optimization problems by quantum computing. Specifically, let f1,...,fn: Rd → R be -smooth convex functions and ψ: Rd → R be a µ-strongly convex proximal function. The goal is to find an ǫ-optimal point for F(x) = n1 Pni=1 fi(x) + ψ(x). We give a quantum algorithm with complexity O(n + √d + p/µ(n1/3d1/3 + n−2/3d5/6))1 improving the classical tight bound Θ(n + pn/µ). We also prove a quantum lower bound Ω( n + n3/4(/µ)1/4) when d is large enough. Both our quantum upper and lower bounds can extend to the cases where ψ is not necessarily stronglyconvex, or each fi is Lipschitz but not necessarily smooth. In addition, when F is nonconvex, our quantum algorithm can find _an ǫ-critial point using O(n + (d1/3n1/3 + √d)/ǫ2) queries. Copyright © 2024, The Authors. All rights reserved.

关键词： Support vector machines

Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFD)to geoscience and climate ***,much effort has been given in combining DA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

Machine Learning and Network Traffic to Distinguish Between Malware and Benign Applications 1st

学校读者我要写书评

暂无评论

Machine Learning and Network Traffic to Distinguish Between ...

1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Abualigah, Laith Abualigah, Sayel Almahmoud, Mothanna Forestiero, Agostino Sachdeva, Gagan Hanandeh, Essam S. Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Department of Computer Information Systems Jordan University of Science and Technology Ar-Ramtha Jordan Performance Computing and Networking National Research Council of Italy Rende87036 Italy First Abu Dhabi Bank Abu Dhabi United Arab Emirates Department of Computer Information System Zarqa University Zarqa Jordan

ISBN: (纸本)9783031314681

Virus detection software is widely used for servers, systems, and devices that seek to maintain security and reliability. Although these programs provide an excellent safety level, the traditional defense methods fail to detect new Malware. The more advanced approach relies on predicting malicious behavior with dynamic analysis of the process executed. This paper presents a new method for detecting malware using machine learning algorithms applied to data obtained from the Cuckoo sandbox. The Cuckoo sandbox isolates the file being analyzed, providing detailed dynamic analysis reports. The machine learning algorithms were compared and the most important features were identified. The results were obtained using six popular classifiers, including SVM, Random Forest, and LightGBM, and the XGBOOST algorithm had the highest accuracy, at an average of 97%. However, the research on machine learning-based malware analysis is limited in terms of computational complexity and detection accuracy. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Software reliability

Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension 39

学校读者我要写书评

暂无评论

Thought-Path Contrastive Learning via Premise-Oriented Data ...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Chenxu Jian, Ping Yang, Zhen School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)157735897X

Logical reading comprehension is a challenging task that involves understanding the underlying semantics of text and applying reasoning to deduce the correct answer. Prior researches have primarily focused on enhancing logical reasoning capabilities through Chain-of-Thought (CoT) or data augmentation. However, previous work constructing chain-of-thought rationales concentrates solely on analyzing correct options, neglecting the incorrect alternatives. Addtionally, earlier efforts on data augmentation by altering contexts rely on rule-based methods, which result in generated contexts that lack diversity and coherence. To address these issues, we propose a Premise-Oriented Data Augmentation (PODA) framework. This framework can generate CoT rationales including analyses for both correct and incorrect options, while constructing diverse and high-quality counterfactual contexts from incorrect candidate options. We integrate summarizing premises and identifying premises for each option into rationales. Subsequently, we employ multi-step prompts with identified premises to construct counterfactual context. To facilitate the model’s capabilities to better differentiate the reasoning process associated with each option, we introduce a novel thought-path contrastive learning method that compares reasoning paths between the original and counterfactual samples. Experimental results on three representative LLMs demonstrate that our method can improve the baselines substantially across two challenging logical reasoning benchmarks (ReClor and LogiQA 2.0). © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

I run as fast as a rabbit, can you? A Multilingual Simile Dialogue Dataset

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Ma, Longxuan Zhang, Weinan Zhou, Shuhan Sun, Churui Ke, Changxin Liu, Ting Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China School of Information Science Beijing Language and Culture University China School of Computer Science Harbin Institute of Technology China

A simile is a figure of speech that compares two different things (called the tenor and the vehicle) via shared properties. The tenor and the vehicle are usually connected with comparator words such as "like" or "as". The simile phenomena are unique and complex in a real-life dialogue scene where the tenor and the vehicle can be verbal phrases or sentences, mentioned by different speakers, exist in different sentences, or occur in reversed order. However, the current simile research usually focuses on similes in a triplet tuple (tenor, property, vehicle) or a single sentence where the tenor and vehicle are usually entities or noun phrases, which could not reflect complex simile phenomena in real scenarios. In this paper, we propose a novel and high-quality multilingual simile dialogue (MSD) dataset to facilitate the study of complex simile phenomena. The MSD is the largest manually annotated simile data (∼20K) and it contains both English and Chinese data. Meanwhile, the MSD data can also be used on dialogue tasks to test the ability of dialogue systems when using similes. We design 3 simile tasks (recognition, interpretation, and generation) and 2 dialogue tasks (retrieval and generation) with MSD. For each task, we provide experimental results from strong pre-trained or state-of-the-art models. The experiments demonstrate the challenge of MSD and we will release the data/code on GitHub. Copyright © 2023, The Authors. All rights reserved.

关键词： Spatio-temporal data

Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph 25

学校读者我要写书评

暂无评论

Way to Specialist: Closing Loop Between Specialized LLM and ...

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Yutong Zhang Lixing Chen Shenghong Li Nan Cao Yang Shi Jiaxin Ding Zhe Qu Pan Zhou Yang Bai School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China College of Design and Innovation Tongji University Shanghai China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering Central South University Changsha Hunan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798400712456

Large language models (LLMs) have demonstrated exceptional performance across a wide variety of domains. Nonetheless, generalist LLMs continue to fall short in reasoning tasks necessitating specialized knowledge, e.g., emotional sociology and medicine. Prior investigations into specialized LLMs focused on domain-specific training, which entails substantial efforts in domain data acquisition and model parameter fine-tuning. To address these challenges, this paper proposes the Way-to-Specialist (WTS) framework, which synergizes retrieval-augmented generation with knowledge graphs (KGs) to enhance the specialized capability of LLMs in the absence of specialized training. In distinction to existing paradigms that merely utilize external knowledge from general KGs or static domain KGs to prompt LLM for enhanced domain-specific reasoning, WTS proposes an innovative ''LLM↻KG'' paradigm, which achieves bidirectional enhancement between specialized LLM and domain knowledge graph (DKG). The proposed paradigm encompasses two closely coupled components: the DKG-Augmented LLM and the LLM-Assisted DKG Evolution. The former retrieves question-relevant domain knowledge from DKG and uses it to prompt LLM to enhance the reasoning capability for domain-specific tasks; the latter leverages LLM to generate new domain knowledge from processed tasks and use it to evolve DKG. WTS closes the loop between DKG-Augmented LLM and LLM-Assisted DKG Evolution, enabling continuous improvement in the domain specialization as it progressively answers and learns from domain-specific questions. We validate the performance of WTS on 7 datasets (e.g., TweetQA, ChatDoctor5k) spanning 6 domains, e.g., emotional sociology, medical, ect. The experimental results show that WTS surpasses the previous SOTA in 5 specialized domains, and achieves a maximum performance improvement of 11.3%.

关键词： domain knowledge graph