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检索条件"机构=CAS Key Laboratory of AI Safety Institute of Computing Technology"

共 132 条记录，以下是1-10 订阅

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GNN-Based Persistent K-core Community Search in Temporal Graphs

GNN-Based Persistent K-core Community Search in Temporal Gra...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Jiang, Zongli Tan, Yirui Chen, Guoxin Guo, Fangda Zhang, Jinli Bai, Xiaolu Beijing University of Technology Beijing China Institute of Computing Technology Cas University of Chinese Academy of Sciences Key Laboratory of Ai Safety Beijing China Institute of Computing Technology Cas Key Laboratory of Ai Safety Beijing China

ISBN: (纸本)9798350362480

The goal of community search is to provide effective solutions for real-time, high-quality community searches within large networks. In many practical applications, such as event organization and friend recommendations, discovering various community structures within a network is crucial for users. However, existing community search algorithms rarely address issues within temporal graphs, and those that do often have two main limitations: (1) traditional community search methods become inefficient and experience significant increases in computation time when scaled to large graphs;(2) while GNN-based community search methods for temporal graphs offer generalizability, they often focus solely on community connectivity and lack cohesiveness. Therefore, we propose a new model PK-GCN, based on Graph Neural Networks (GNNs), to identify persistent k-core communities in temporal networks. This model can handle dynamic changes in temporal graphs and identify communities that persist over time. Compared to existing community search methods, our model not only finds communities with tighter structures but also allows for dynamic queries based on user input without needing retraining. Specifically, our model constructs features by integrating k-core information from core decomposition, graph features, and query features, resulting in more expressive node representations. Additionally, we designed a flexible dynamic query mechanism that allows users to input time information to query communities. Experiments on multiple datasets demonstrate that our model outperforms other GNN-based community search algorithms in F1-score. © 2024 IEEE.

关键词： Spatio-temporal data

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CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense 38

CausalDiff: Causality-Inspired Disentanglement via Diffusion...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Mingkun Bi, Keping Chen, Wei Chen, Quanrun Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology CAS China Key Laboratory of Network Data Science and Technology Institute of Computing Technology CAS China School of Statistics University of International Business and Economics China

Despite ongoing efforts to defend neural classifiers from adversarial attacks, they remain vulnerable, especially to unseen attacks. In contrast, humans are difficult to be cheated by subtle manipulations, since we make judgments only based on essential factors. Inspired by this observation, we attempt to model label generation with essential label-causative factors and incorporate label-non-causative factors to assist data generation. For an adversarial example, we aim to discriminate the perturbations as non-causative factors and make predictions only based on the label-causative factors. Concretely, we propose a casual diffusion model (CausalDiff) that adapts diffusion models for conditional data generation and disentangles the two types of casual factors by learning towards a novel casual information bottleneck objective. Empirically, CausalDiff has significantly outperformed state-of-the-art defense methods on various unseen attacks, achieving an average robustness of 86.39% (+4.01%) on CIFAR-10, 56.25% (+3.13%) on CIFAR-100, and 82.62% (+4.93%) on GTSRB (German Traffic Sign Recognition Benchmark). The code is available at https://***/cas-aisafetyBasicResearchGroup/CausalDiff. © 2024 Neural information processing systems foundation. All rights reserved.

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Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models 31

Teaching-Inspired Integrated Prompting Framework: A Novel Ap...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Tan, Wenting Chen, Dongxiao Xue, Jieting Wang, Zihao Chen, Taijie Key Laboratory of AI Safety Institute of Computing Technology CAS China NetEase Youdao The University of Hong Kong Hong Kong

ISBN: (纸本)9798891761971

Large Language Models (LLMs) exhibit impressive performance across various domains but still struggle with arithmetic reasoning tasks. Recent work shows the effectiveness of prompt design methods in enhancing reasoning capabilities. However, these approaches overlook crucial requirements for prior knowledge of specific concepts, theorems, and tricks to tackle most arithmetic reasoning problems successfully. To address this issue, we propose a novel and effective Teaching-Inspired Integrated Prompting Framework, which emulates the instructional process of a teacher guiding students. This method equips LLMs with essential concepts, relevant theorems, and similar problems with analogous solution approaches, facilitating the enhancement of reasoning abilities. Additionally, we introduce two new Chinese datasets, MathMC and MathToF, both with detailed explanations and answers. Experiments are conducted on nine benchmarks which demonstrates that our approach improves the reasoning accuracy of LLMs. With GPT-4 and our framework, we achieve new state-of-the-art performance on four math benchmarks (AddSub, SVAMP, Math23K and AQuA) with accuracies of 98.2% (+3.3%), 93.9% (+0.2%), 94.3% (+7.2%) and 81.1% (+1.2%). ©2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Plot Retrieval as an Assessment of Abstract Semantic Association 62

Plot Retrieval as an Assessment of Abstract Semantic Associa...

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62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Xu, Shicheng Pang, Liang Li, Jiangnan Yu, Mo Meng, Fandong Shen, Huawei Cheng, Xueqi Zhou, Jie CAS Key Laboratory of AI Security Institute of Computing Technology CAS China Pattern Recognition Center WeChat AI

ISBN: (纸本)9798891760974

Retrieving relevant plots from the book for a query is a critical task, which can improve the reading experience and efficiency of readers. Readers usually only give an abstract and vague description as the query based on their own understanding, summaries, or speculations of the plot, which requires the retrieval model to have a strong ability to estimate the abstract semantic associations between the query and candidate plots. However, existing information retrieval (IR) datasets cannot reflect this ability well. In this paper, we propose PLOTRETRIEVAL, a labeled dataset to train and evaluate the performance of IR models on the novel task Plot Retrieval. Text pairs in PLOTRETRIEVAL have less word overlap and more abstract semantic association, which can reflect the ability of the IR models to estimate the abstract semantic association, rather than just traditional lexical or semantic matching. Extensive experiments across various lexical retrieval, sparse retrieval, dense retrieval, and cross-encoder methods compared with human studies on PLOTRETRIEVAL show current IR models still struggle in capturing abstract semantic association between texts. PLOTRETRIEVAL can be the benchmark for further research on the semantic association modeling ability of IR models. ©2024 Association for Computational Linguistics.

关键词： Structured Query Language

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The Fall of ROME: Understanding the Collapse of LLMs in Model Editing

The Fall of ROME: Understanding the Collapse of LLMs in Mode...

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

作者： Yang, Wanli Sun, Fei Tan, Jiajun Ma, Xinyu Su, Du Yin, Dawei Shen, Huawei CAS Key Laboratory of AI Safety Institute of Computing Technology CAS China University of Chinese Academy of Sciences China Baidu Inc. China

ISBN: (纸本)9798891761681

Despite significant progress in model editing methods, their application in real-world scenarios remains challenging as they often cause large language models (LLMs) to collapse. Among them, ROME is particularly concerning, as it could disrupt LLMs with only a single edit. In this paper, we study the root causes of such collapse. Through extensive analysis, we identify two primary factors that contribute to the collapse: i) inconsistent handling of prefixed and unprefixed keys in the parameter update equation may result in very small denominators, causing excessively large parameter updates;ii) the subject of collapse cases is usually the first token, whose unprefixed key distribution significantly differs from the prefixed key distribution in autoregressive transformers, causing the aforementioned issue to materialize. To validate our findings, we propose a simple yet effective approach: uniformly using prefixed keys during editing phase and adding prefixes during testing phase to ensure the consistency between training and testing. The experimental results show that the proposed solution can prevent model collapse while maintaining the effectiveness of the edits. © 2024 Association for Computational Linguistics.

关键词： Distribution transformers

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Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration

Enhancing Training Data Attribution for Large Language Model...

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

作者： Wu, Kangxi Pang, Liang Shen, Huawei Cheng, Xueqi Key Laboratory of AI Safety Chinese Academy of Sciences Institute of Computing Technology CAS China University of Chinese Academy of Sciences China

ISBN: (纸本)9798891761643

The black-box nature of large language models (LLMs) poses challenges in interpreting results, impacting issues such as data intellectual property protection and hallucination tracing. Training data attribution (TDA) methods are considered effective solutions to address these challenges. Most recent TDA methods rely on influence functions, assuming the model achieves minimized empirical risk. However, achieving this criterion is difficult, and sourcing accuracy can be compromised by fitting errors during model training. In this paper, we introduce a novel TDA method called Debias and Denoise Attribution (DDA), which enhances influence functions by addressing fitting errors. Specifically, the debias strategy seeks to improve the performance of influence functions by eliminating the knowledge bias present in the base model before fine-tuning, while the denoise strategy aims to reduce discrepancies in influence scores arising from varying degrees of fitting during the training process through smoothing techniques. Experimental results demonstrate that our method significantly outperforms existing approaches, achieving an averaged AUC of 91.64%. Moreover, DDA exhibits strong generality and scalability across various sources and different-scale models like LLaMA2, QWEN2, and Mistral. © 2024 Association for Computational Linguistics.

关键词： Digital elevation model

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Personalized Denoising Implicit Feedback for Robust Recommender System 25

Personalized Denoising Implicit Feedback for Robust Recommen...

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34th ACM Web Conference, WWW 2025

作者： Zhang, Kaike Cao, Qi Wu, Yunfan Sun, Fei Shen, Huawei Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400712746

While implicit feedback is foundational to modern recommender systems, factors such as human error, uncertainty, and ambiguity in user behavior inevitably introduce significant noise into this feedback, adversely affecting the accuracy and robustness of recommendations. To address this issue, existing methods typically aim to reduce the training weight of noisy feedback or discard it entirely, based on the observation that noisy interactions often exhibit higher losses in the overall loss distribution. However, we identify two key issues: (1) there is a significant overlap between normal and noisy interactions in the overall loss distribution, and (2) this overlap becomes even more pronounced when transitioning from pointwise loss functions (e.g., BCE loss) to pairwise loss functions (e.g., BPR loss). This overlap leads traditional methods to misclassify noisy interactions as normal, and vice versa. To tackle these challenges, we further investigate the loss overlap and find that for a given user, there is a clear distinction between normal and noisy interactions in the user’s personal loss distribution. Based on this insight, we propose a resampling strategy to Denoise using the user’s Personal Loss distribution, named PLD, which reduces the probability of noisy interactions being optimized. Specifically, during each optimization iteration, we create a candidate item pool for each user and resample the items from this pool based on the user’s personal loss distribution, prioritizing normal interactions. Additionally, we conduct a theoretical analysis to validate PLD’s effectiveness and suggest ways to further enhance its performance. Extensive experiments conducted on three datasets with varying noise ratios demonstrate PLD’s efficacy and robustness. © 2025 Copyright held by the owner/author(s).

关键词： Normal distribution

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Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation 38

Understanding and Improving Adversarial Collaborative Filter...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Kaike Cao, Qi Wu, Yunfan Sun, Fei Shen, Huawei Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China

Adversarial Collaborative Filtering (ACF), which typically applies adversarial perturbations at user and item embeddings through adversarial training, is widely recognized as an effective strategy for enhancing the robustness of Collaborative Filtering (CF) recommender systems against poisoning attacks. Besides, numerous studies have empirically shown that ACF can also improve recommendation performance compared to traditional CF. Despite these empirical successes, the theoretical understanding of ACF's effectiveness in terms of both performance and robustness remains unclear. To bridge this gap, in this paper, we first theoretically show that ACF can achieve a lower recommendation error compared to traditional CF with the same training epochs in both clean and poisoned data contexts. Furthermore, by establishing bounds for reductions in recommendation error during ACF's optimization process, we find that applying personalized magnitudes of perturbation for different users based on their embedding scales can further improve ACF's effectiveness. Building on these theoretical understandings, we propose Personalized Magnitude Adversarial Collaborative Filtering (PamaCF). Extensive experiments demonstrate that PamaCF effectively defends against various types of poisoning attacks while significantly enhancing recommendation performance. © 2024 Neural information processing systems foundation. All rights reserved.

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PKAD: Pretrained Knowledge is All You Need to Detect and Mitigate Textual Backdoor Attacks

PKAD: Pretrained Knowledge is All You Need to Detect and Mit...

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

作者： Chen, Yu Cao, Qi Zhang, Kaike Liu, Xuchao Shen, Huawei CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761681

In textual backdoor attacks, attackers insert poisoned samples with triggered inputs and target labels into training datasets to manipulate model behavior, threatening the model's security and reliability. Current defense methods can generally be categorized into inference-time and training-time ones. The former often requires a part of clean samples to set detection thresholds, which may be hard to obtain in practical application scenarios, while the latter usually requires an additional retraining or unlearning process to get a clean model, significantly increasing training costs. To avoid these drawbacks, we focus on developing a practical defense method before model training without using any clean samples. Our analysis reveals that with the help of a pretrained language model (PLM), poisoned samples, different from clean ones, exhibit mismatched relationship and shared characteristics. Based on these observations, we further propose a two-stage poison detection strategy solely leveraging insights from PLM before model training. Extensive experiments confirm our approach's effectiveness, achieving better performance than current leading methods more swiftly. Our code is available at https://***/Ascian/PKAD. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Precise Integral in NeRFs: Overcoming the Approximation Errors of Numerical Quadrature

Precise Integral in NeRFs: Overcoming the Approximation Erro...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Zhang, Boyuan He, Zhenliang Kan, Meina Shan, Shiguang Institute of Computing Technology Cas Key Lab of Ai Safety China University of Chinese Academy of Sciences China

ISBN: (纸本)9798331510831

Neural Radiance Fields (NeRFs) use neural networks to translate spatial coordinates to corresponding volume density and directional radiance, enabling realistic novel view synthesis through volume rendering. Rendering new viewpoints involves computing volume rendering integrals along rays, usually approximated by numerical quadrature because of lacking closed-form solutions. In this paper, utilizing Taylor expansion, we demonstrate that numerical quadrature causes inevitable approximation error in NeRF integrals due to ignoring the parameter associated with the Lagrange remainder. To mitigate the approximation error, we propose a novel neural field with segment representation as input to implicitly model the remainder parameter. In theory, our proposed method is proven to possess the potential to achieve fully precise rendering integral, as demonstrated by comprehensive experiments on several commonly used datasets with state-of-the-art results. © 2025 IEEE.

关键词： Rendering (computer graphics)

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