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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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Confidence Aware Learning for Reliable Face Anti-spoofing

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5083-5093页

作者： Long, Xingming Zhang, Jie Shan, Shiguang Key Laboratory of AI Safety of CAS Institute of Computing Technology (ICT) Chinese Academy of Sciences (CAS) Beijing China University of Chinese Academy of Sciences (UCAS) Beijing China

Current Face Anti-spoofing (FAS) models tend to make overly confident predictions even when encountering unfamiliar scenarios or unknown presentation attacks, which leads to serious potential risks. To solve this problem, we propose a Confidence Aware Face Anti-spoofing (CA-FAS) model, which is aware of its capability boundary, thus achieving reliable liveness detection within this boundary. To enable the CA-FAS to "know what it doesn’t know", we propose to estimate its confidence during the prediction of each sample. Specifically, we build Gaussian distributions for both the live faces and the known attacks. The prediction confidence for each sample is subsequently assessed using the Mahalanobis distance between the sample and the Gaussians for the "known data". We further introduce the Mahalanobis distance-based triplet mining to optimize the parameters of both the model and the constructed Gaussians as a whole. Extensive experiments show that the proposed CA-FAS can effectively recognize samples with low prediction confidence and thus achieve much more reliable performance than other FAS models by filtering out samples that are beyond its reliable range. © 2005-2012 IEEE.

关键词： Gaussian distribution

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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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"Not Aligned" is Not "Malicious": Being Careful about Hallucinations of Large Language Models' Jailbreak 31

"Not Aligned" is Not "Malicious": Being Careful about Halluc...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Mei, Lingrui Liu, Shenghua Wang, Yiwei Bi, Baolong Mao, Jiayi Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology CAS China University of Chinese Academy of Sciences China UCLA United States University of California Merced United States Tsinghua University China

ISBN: (纸本)9798891761964

"Jailbreak" is a major safety concern of Large Language Models (LLMs), which occurs when malicious prompts lead LLMs to produce harmful outputs, raising issues about the reliability and safety of LLMs. Therefore, an effective evaluation of jailbreaks is very crucial to develop its mitigation strategies. However, our research reveals that many jailbreaks identified by current evaluations may actually be hallucinations-erroneous outputs that are mistaken for genuine safety breaches. This finding suggests that some perceived vulnerabilities might not represent actual threats, indicating a need for more precise red teaming benchmarks. To address this problem, we propose the Benchmark for reliABilitY and jailBreak haLlUcination Evaluation (BABYBLUE). BABYBLUE introduces a specialized validation framework including various evaluators to enhance existing jailbreak benchmarks, ensuring outputs are useful malicious instructions. Additionally, BABYBLUE presents a new dataset as an augmentation to the existing red teaming benchmarks, specifically addressing hallucinations in jailbreaks, aiming to evaluate the true potential of jailbroken LLM outputs to cause harm to human society. © 2025 Association for Computational Linguistics.

关键词： Benchmarking

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LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework 25

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18th ACM International Conference on Web Search and Data Mining, WSDM 2025

作者： Qiao, Yiran Ao, Xiang Liu, Yang Xu, Jiarong Sun, Xiaoqian He, Qing Institute of Computing Technology CAS Beijing China Key Lab of AI Safety Chinese Academy of Sciences Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences CAS Beijing China School of Management Fudan University Shanghai China Institute of Intelligent Computing Technology Suzhou China

ISBN: (纸本)9798400713293

Recent prevailing works on graph machine learning typically follow a similar methodology that involves designing advanced variants of graph neural networks (GNNs) to maintain the superior performance of GNNs on different graphs. In this paper, we aim to streamline the GNN design process and leverage the advantages of Large Language Models (LLMs) to improve the performance of GNNs on downstream tasks. We formulate a new paradigm, coined "LLMs-as-Consultants", which integrates LLMs with GNNs in an interactive manner. A framework named LOGIN (LLM cOnsulted GNN traiNing) is instantiated, empowering the interactive utilization of LLMs within the GNN training process. First, we attentively craft concise prompts for spotted nodes, carrying comprehensive semantic and topological information, and serving as input to LLMs. Second, we refine GNNs by devising a complementary coping mechanism that utilizes the responses from LLMs, depending on their correctness. We empirically evaluate the effectiveness of LOGIN on node classification tasks across both homophilic and heterophilic graphs. The results illustrate that even basic GNN architectures, when employed within the proposed LLMs-as-Consultants paradigm, can achieve comparable performance to advanced GNNs with intricate designs. Our code is available at https://***/QiaoYRan/LOGIN. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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ELabrador: A Wearable Navigation System for Visually Impaired Individuals

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 12228-12244页

作者： Kan, Meina Zhang, Lixuan Liang, Hao Zhang, Boyuan Fang, Minxue Liu, Dongyang Shan, Shiguang Chen, Xilin Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Ai Safety Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Peng Cheng National Laboratory Shenzhen518055 China

Visually impaired individuals encounter significant challenges when walking and acting in unfamiliar environments, particularly in outdoor scenarios. The complexity of outdoor environments, characterized by diverse obstacles, traffic signals, and societal norms, poses substantial barriers to mobility of visually impaired individuals and makes long-distance walking especially arduous. Although GPS-based navigation systems can facilitate long-distance travel, they often suffer from location inaccuracies in urban areas and even completely fail indoors. Moreover, these systems lack the capability to provide detailed information about walkways and immediate surroundings, which are crucial for safe and efficient walking. To address these limitations, we introduce a proof-of-concept wearable navigation system named eLabrador, designed to assist visually impaired individuals in long-distance walking in unfamiliar outdoor environments. The eLabrador integrates public maps (e.g. Amap or Google Maps) and GPS for global route planning, while leveraging computational visual perception to provide precise and safe local guidance. This hybrid approach enables accurate and safe navigation for visually impaired individuals in outdoor scenarios. Specifically, the eLabrador utilizes a head-mounted RGB-D camera to capture environmental geometric terrain and objects in outdoor urban environments. These inputs are processed into a 3D semantic map, offering a detailed representation of the surrounding environment. The planning module then integrates this 3D semantic map with route information from the global map (i.e. Amap) to generate an optimized walking path. Finally, the interaction module utilizes the audio-haptic dual-channel to relay navigation instructions to visually impaired user. Together, these three modules work seamlessly to facilitate long-distance navigation for visually impaired individuals in outdoor environments. The eLabrador is evaluated with two real-world outdoor sce

关键词： Traffic signals

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SPEAR: A Structure-Preserving Manipulation Method for Graph Backdoor Attacks 34

SPEAR: A Structure-Preserving Manipulation Method for Graph ...

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34th ACM Web Conference, WWW 2025

作者： Ding, Yuanhao Liu, Yang Ji, Yugang Wen, Weigao He, Qing Ao, Xiang Institute of Computing Technology Chinese Academy of Sciences Beijing China Hangzhou China Key Laboratory of AI Safety Chinese Academy of Sciences China Institute of Intelligent Computing Technology CAS Suzhou China University of Chinese Academy of Sciences CAS China

ISBN: (纸本)9798400712746

Graph Neural Networks (GNNs) are vulnerable to backdoor attacks, where adversaries implant malicious triggers to manipulate model predictions. Existing graph backdoor attacks are susceptible to defense mechanisms or robust classifiers because they rely on subgraph injection or structural perturbations, e.g., creating additional edges to attach backdoor triggers to the original graph. To enhance the stealthiness of graph backdoors, we propose SPEAR, a novel structure-preserving graph backdoor attack that avoids modifying the graph’s topology. SPEAR operates within a limited attack budget by selectively perturbing node attributes while ensuring the triggers exert significant influence through a global importance-driven feature selection strategy. Additionally, a neighborhood-aware trigger generator is employed to underpin a high attack success rate by utilizing semantic information from the neighborhood. SPEAR amplifies effectiveness and stealthiness by combining subtle yet impactful attribute manipulation with a refined trigger generation mechanism. Extensive experiments demonstrate that SPEAR achieves state-of-the-art effectiveness in bypassing defenses on real-world datasets, establishing it as a potent and stealthy backdoor attack for graph-based tasks. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial Attack Backdoor Attack Graph Neural Network

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Personalized Denoising Implicit Feedback for Robust Recommender System

arXiv

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arXiv 2025年

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

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. Copyright © 2025, The Authors. All rights reserved.

关键词： Normal distribution

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Personalized Denoising Implicit Feedback for Robust Recommender System 34

Personalized Denoising Implicit Feedback for Robust Recommen...

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34th ACM Web Conference, WWW 2025

ISBN: (纸本)9798400712746

关键词： Denoising Recommendation Implicit Feedback Robust Recommender System

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Unifying Bias and Unfairness in Information Retrieval: New Challenges in the LLM Era 25

Unifying Bias and Unfairness in Information Retrieval: New C...

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18th ACM International Conference on Web Search and Data Mining, WSDM 2025

作者： Dai, Sunhao Xu, Chen Xu, Shicheng Pang, Liang Dong, Zhenhua Xu, Jun Gaoling School of Artificial Intelligence Renmin University of China Beijing China CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China Huawei Noah's Ark Lab Shenzhen China

ISBN: (纸本)9798400713293

With the rapid advancements of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a paradigm shift due to their integration. However, integrating LLMs into the IR pipelines has also introduced new challenges, particularly in the form of biases and unfairness that may disrupt the information ecosystem. This tutorial will offer a comprehensive overview of emerging and pressing bias and unfairness issues associated with integrating LLMs into IR systems. Specifically, this tutorial first unifies bias and unfairness issues as problems of distribution mismatch and further categorizes the mitigation strategies under the umbrella of distribution alignment. Then, we summarize several types of bias and unfairness issues emerging from three critical stages of LLM integration into IR systems: data collection, model development, and result evaluation. We will systematically review and analyze their definitions, characteristics, and corresponding mitigation strategies in recent literature. Finally, we will highlight some open problems and future research directions. We hope this tutorial can raise the awareness of researchers and stakeholders in the IR field and beyond regarding bias and unfairness issues in this LLM era. © 2025 Copyright held by the owner/author(s).

关键词： Recommender systems

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