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31st International Conference on Computational Linguistics, COLING 2025

作者： Yue, Yuanhao Wang, Chengyu Huang, Jun Wang, Peng School of Computer Science Fudan University Shanghai China Alibaba Cloud Computing Hangzhou China

ISBN: (纸本)9798891761971

Specializing LLMs in various domain-specific tasks has emerged as a critical step towards achieving high performance. However, the construction and annotation of datasets in specific domains are always very costly. Apart from using superior and expensive closed-source LLM APIs to construct datasets, some open-source models have become strong enough to handle dataset construction in many scenarios. Thus, we present a family of data augmentation models designed to significantly improve the efficiency for model fine-tuning. These models, trained based on sufficiently small LLMs, support key functionalities with low inference costs: instruction expansion, instruction refinement, and instruction-response pair expansion. To fulfill this goal, we first construct an automatic data collection system with seed datasets generated from both public repositories and our in-house datasets. This system leverages powerful LLMs to expand, refine and re-write the instructions and responses, incorporating quality assessment techniques. Following this, we introduce the training process of our models, which effectively distills task-solving and text synthesis abilities from teacher LLMs. Finally, we demonstrate how we integrate these functionalities into a machine learning platform to support low-cost LLM fine-tuning from both dataset preparation and training perspectives for users. Experiments and an application study prove the effectiveness of our approach. ©2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Which is more faithful,seeing or saying? Multimodal sarcasm detection exploiting contrasting sentiment knowledge

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CAAI Transactions on Intelligence Technology 2025年第2期10卷 375-386页

作者： Yutao Chen Shumin Shi Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly ***,detecting sarcasm in various forms of communication can be difficult due to conflicting *** this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and ***,five external sentiments are introduced to prompt the model learning sentimental preferences among ***,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike *** results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.

关键词： CLIP image-text classification knowledge fusion multi-modal sarcasm detection

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Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

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computers, Materials & Continua 2024年第9期80卷 4663-4686页

作者： Aljuaid Turkea Ayedh M Ainuddin Wahid Abdul Wahab Mohd Yamani Idna Idris Faculty of Computer Science and Information Technology Universiti MalayaKuala Lumpur50603Malaysia Faculty of Computer Science and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Center for Mobile Cloud Computing Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala Lumpur50603Malaysia

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce ***,this trend introduces security challenges,such as unauthorized *** access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and *** paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN *** technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access *** proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy ***,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern ***,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

关键词： BYOD security access control access control decision-enforcement deep learning neural network techniques TabularDNN multilayer dynamic adaptable flexibility bottlenecks performance policy conflict

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Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning

Distilling Instruction-following Abilities of Large Language...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yue, Yuanhao Wang, Chengyu Huang, Jun Wang, Peng School of Computer Science Fudan University Shanghai China Alibaba Cloud Computing Hangzhou China

ISBN: (纸本)9798891761681

Instruction tuning aims to align large language models (LLMs) with open-domain instructions and human-preferred responses. While several studies have explored autonomous approaches to distilling and annotating instructions from powerful proprietary LLMs, such as ChatGPT, they often neglect the impact of the distributions and characteristics of tasks, together with the varying difficulty of instructions in training sets. This oversight can lead to imbalanced knowledge capabilities and poor generalization powers of student LLMs. To address these challenges, we introduce Task-Aware Curriculum Planning for Instruction Refinement (TAPIR), a multi-round distillation framework that utilizes an oracle LLM to select instructions that are difficult for a student LLM to follow. To balance the student's capabilities, task distributions in training sets are adjusted with responses automatically refined according to their corresponding tasks. In addition, by incorporating curriculum planning, our approach systematically escalates the difficulty levels of tasks, progressively enhancing the student LLM's capabilities. We rigorously evaluate TAPIR using several widely recognized benchmarks (such as AlpacaEval 2.0, MT-Bench, etc.) and multiple student LLMs. Empirical results demonstrate that student LLMs, trained with our method and less training data, outperform larger instruction-tuned models and strong distillation baselines. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Inductive Relation Prediction by Disentangled Subgraph Structure

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Tsinghua science and Technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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Efficient Quality Controllable Neural Image Compression based on QD-Model

Efficient Quality Controllable Neural Image Compression base...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Shaokang Xiang, Guoqing Xu, Jinchang Zhang, Shanghang Xie, Xiaodong School of Computer Science Peking University Beijing China Alibaba Cloud Computing Beijing China

ISBN: (纸本)9798350368741

Neural image compression has achieved significant advancements, consistently outperforming traditional codecs in terms of performance. However, research on quality control algorithms for neural image compression is still lacking. In this paper, we propose a framework designed to control the quality of compressed images through a one-pass pre-analysis. First, we construct a foundational relationship between the quantization factor and compression distortion, utilizing variable rate neural image compression as the basis for quality control. Second, we introduce the image Content-Compression features-based Distortion Estimation Network (C2DEN) to efficiently fit the sample-adaptive Quantization-Distortion (QD) model. Leveraging the QD model, we convert the target quality into a quantization factor to control the compression model, enabling quality-controllable compression of samples. Experimental results show that the average quality errors on four different datasets are only 0.89%, 1.79%, 1.73%, and 1.61%. Compared with existing control methods, our method reduces the algorithm time complexity by 98.58%, 98.52%, 98.85%, and 98.50% while ensuring accuracy, which further demonstrates the superiority of our method. © 2025 IEEE.

关键词： linear model Neural image compression quality control quantization factor

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Decision making framework for heterogeneous QoS information: an application to cloud service selection

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Journal of Ambient Intelligence and Humanized computing 2023年第3期14卷 2915-2934页

作者： Tiwari, Rohit Kumar Kumar, Rakesh Baranwal, Gaurav Buyya, Rajkumar Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur273010 India Department of Computer Science BHU Varanasi India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne Australia

In recent times, appropriate decision-making in challenging and critical situations has been very well supported by multicriteria decision-making (MCDM) methods. The technique for order of preference by similarity to ideal solution (TOPSIS) is the most widely used MCDM method for solving decision problems. However, it restricts decision-makers to use only one type of Quality of Service (QoS) information, and it suffers from the rank reversal problem. Restriction to only one type of QoS makes the decision problems more challenging, as it restricts the decision-makers freedom. Further, the rank reversal problem makes the decision result unreliable. To address these issues of TOPSIS, we have proposed a reliable rank reversal robust modular TOPSIS (RMo-TOPSIS). RMo-TOPSIS allows crisp, interval, fuzzy, intuitionistic and neutrosophic fuzzy QoS metrics. It does not suffer from the rank reversal problem. cloud computing provides computing services on-demand basis without involving maintenance by its users. The availability of many cloud service providers and their services makes cloud service selection a challenging problem. To validate RMo-TOPSIS, we select the cloud service selection consisting of different types of QoS metrics as an application. Experiments on cloud service selection show consistency and accuracy in results obtained by RMo-TOPSIS and its robustness against rank reversal. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision making

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Knowledge-enriched joint-learning model for implicit emotion cause extraction

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 118-128页

作者： Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

关键词： emotion cause extraction external knowledge fusion implicit emotion recognition joint learning

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TSM-LLM: Task Scheduling Management System for Large Language Models 5

TSM-LLM: Task Scheduling Management System for Large Languag...

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5th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Wen, Ziqiang Zhu, Guoping Wang, Yong Luo, Haijun Liu, Nianchao Beijing University of Posts and Telecommunications School of Computer Science Beijing China China Electronics Cloud Computing Technology Co. Ltd Cloud Product R&d Department Wuhan China

ISBN: (纸本)9798331506612

As large model services gain popularity;the high cost of deployment presents a challenge. Thus, focusing on improving model efficiency in high-load scenarios while managing the scheduling of multiple models. This paper introduces TSMLLM, a Task Scheduling Management System designed for optimizing the deployment of large language models (LLMs). The proposed system leverages an adaptive time window to group realtime requests, optimizing the batching process for better resource utilization. A two-step scheduling method, involving filtering and scoring, is employed to tailor task distribution based on the specific characteristics of each large model. The system's scalable architecture supports seamless expansion to accommodate evolving model requirements. Experimental results demonstrate a significant increase in throughput with only a slight rise in response time, contributing to a reduction in overall service costs. © 2024 IEEE.

关键词： Batch data processing

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Themis: Adaptive CPU Resource Allocation for VMs on Heterogeneous Multicore Architectures 10

Themis: Adaptive CPU Resource Allocation for VMs on Heteroge...

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10th IEEE Smart World Congress, SWC 2024

作者： Yang, Yecheng Pang, Pu Wang, Jiawen Chen, Quan Chen, Weiwei Li, Li Guo, Minyi Shanghai Jiao Tong University Department of Computer Science and Engineering China Shuhai Lab Huawei Cloud Computing Technologies Co. Ltd China

ISBN: (纸本)9798331520861

Tenants usually rent virtual machines (VMs) to deploy their applications, while those VMs are generally colocated on the same physical machine by the cloud provider to improve resource utilization. Meanwhile, modern CPUs are evolving towards heterogeneous multicore architecture, which integrates powerful brawny cores and power-efficient wimpy cores into a CPU of a machine. This paper focuses on how to allocate CPU resources when co-locating VMs on the heterogeneous architecture. When a VM is created by the Kernel-based Virtual Machine (KVM) virtualization technology, it consists of vCPU threads and Emulator threads. Our investigation shows that properly setting the range of CPU cores where Emulator threads are allowed to run can further improve the VM performance, when using Linux Completely Fair Scheduler (CFS) to schedule VMs. However, it is challenging to find the proper range setting because running different applications in the VM leads to nonidentical optimal range settings, and the VM is usually a black-box from the perspective of the cloud provider. We therefore propose Themis, a runtime system comprising a core scheduler and a load monitor. The core scheduler finds the proper range setting based on low-level information, while the load monitor identifies whether the load of the VM is significantly changed and the range is required to be adjusted. The experimental results show that Themis improves the performance of popular cloud applications by up to 48.4% compared with prior work. © 2024 IEEE.

关键词： Resource allocation

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