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33rd ACM Web Conference, WWW 2024

作者： Xu, Shicheng Pang, Liang Shen, Huawei Cheng, Xueqi Chua, Tat-Seng CAS Key Laboratory of AI Security Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China CAS Key Laboratory of AI Security Institute of Computing Technology Chinese Academy of Sciences Beijing China Sea-NExT Joint Lab National University of Singapore Singapore Singapore

ISBN: (纸本)9798400701719

Making the contents generated by Large Language Model (LLM), accurate, credible and traceable is crucial, especially in complex knowledge-intensive tasks that require multi-step reasoning and each step needs knowledge to solve. Retrieval-augmented generation is good potential to solve this problem. However, where and how to introduce Information Retrieval (IR) to LLM is a big challenge. Previous work has the problems that wrong knowledge retrieved by IR misleads the LLM and interaction between IR and LLM breaks the reasoning chain of LLM. This paper proposes a novel framework named Search-in-the-Chain (SearChain) for the interaction between LLM and IR to solve the challenges. First, LLM generates the reasoning chain named Chain-of-Query (CoQ) where each node consists of an IR-oriented query-answer pair. Second, IR verifies the answer of each node of CoQ. It corrects the answer that is not consistent with the retrieved information when IR gives high confidence, which improves the credibility. Third, LLM can indicate its missing knowledge in CoQ and rely on IR to provide this knowledge to LLM. These operations improve the accuracy in terms of reasoning and knowledge. Finally, SearChain generates the reasoning process and marks references to supporting documents for each reasoning step, which improves traceability. Interaction with IR in SearChain forms a novel reasoning path based on a tree, which enables LLM to dynamically modify the direction of reasoning. Experiments show that SearChain outperforms state-of-the-art baselines on complex knowledge-intensive tasks including multi-hop Q&A, slot filling, fact checking, and long-form Q&A. © 2024 Owner/Author.

关键词： Query processing

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SACH: Significant-Attributed Community Search in Heterogeneous Information Networks 40

SACH: Significant-Attributed Community Search in Heterogeneo...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Liu, Yanghao Guo, Fangda Xu, Bingbing Bao, Peng Shen, Huawei Cheng, Xueqi Institute of Computing Technology CAS Key Laboratory of AI Safety & Security Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350317152

Community search is a personalized community discovery problem aimed at finding densely-connected subgraphs containing the query vertex. In particular, the search for com-munities with high-importance vertices has recently received a great deal of attention. However, existing works mainly focus on conventional homogeneous networks where vertices are of the same type, but are not applicable to heterogeneous information networks (HINs) composed of multi-typed vertices and different semantic relations, such as bibliographic networks. In this paper, we study the problem of high-importance community search in HINs. A novel community model is introduced, named heterogeneous significant community (HSC), to unravel the closely connected vertices of the same type with high attribute values through multiple semantic relationships. An HSC not only maximizes the exploration of indirect relationships across entities of the anchor-type but incorporates their significance. To search the HSCs, we first develop online algorithms by exploiting both segmented-based meta-path expansion and significance incrernent. Specially, a solution space reuse strategy based on structural nesting is designed to boost the efficiency. In addition, we further devise a two-level index to support searching HSCs in optimal time, based on which a space-efficient compact index is proposed. Extensive experiments on real-world large-scale HINs demonstrate that our solutions are effective and efficient for searching HSCs, and the index-based algorithms are 2-4 orders of magnitude faster than online algorithms. © 2024 IEEE.

关键词： Semantics

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Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue

Think Before You Speak: Cultivating Communication Skills of ...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Zhou, Junkai Pang, Liang Shen, Huawei Cheng, Xueqi CAS Key Laboratory of AI Security Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761193

The emergence of large language models (LLMs) further improves the capabilities of open-domain dialogue systems and can generate fluent, coherent, and diverse responses. However, LLMs still lack a crucial ability: communication skills. This limitation renders them more like information seeking tools rather than anthropomorphic chatbots. Communication skills, such as topic transition, proactively asking questions, concept guidance, empathy, and summarising often should be taken into consideration, to make LLMs more anthropomorphic and proactive during the conversation, thereby increasing the interest of users and attracting them to chat for longer. However, enabling these communication skills in black-box LLMs remains a key challenge because they do not have the same utterance formation mode as real people: think before speaking. Inspired by linguistics and cognitive science, we empower LLMs with communication skills through inner monologues. To evaluate various communication skills, we construct a benchmark named Cskills, which can also more comprehensively evaluate the dialogue generation ability of the model. Experimental results show that the proposed CSIM strategy improves the backbone models and outperforms the baselines. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion 38

PDE+: Enhancing Generalization via PDE with Adaptive Distrib...

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38th AAai Conference on Artificial Intelligence, AAai 2024

作者： Yuan, Yige Xu, Bingbing Lin, Bo Hou, Liang Sun, Fei Shen, Huawei Cheng, Xueqi CAS Key Laboratory of AI Safety & Security Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Mathematics National University of Singapore Singapore

ISBN: (纸本)1577358872

The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones. Current methods, mainly based on the data-driven paradigm such as data augmentation, adversarial training, and noise injection, may encounter limited generalization due to model non-smoothness. In this paper, we propose to investigate generalization from a Partial Differential Equation (PDE) perspective, aiming to enhance it directly through the underlying function of neural networks, rather than focusing on adjusting input data. Specifically, we first establish the connection between neural network generalization and the smoothness of the solution to a specific PDE, namely transport equation. Building upon this, we propose a general framework that introduces adaptive distributional diffusion into transport equation to enhance the smoothness of its solution, thereby improving generalization. In the context of neural networks, we put this theoretical framework into practice as PDE+ (PDE with Adaptive Distributional Diffusion) which diffuses each sample into a distribution covering semantically similar inputs. This enables better coverage of potentially unobserved distributions in training, thus improving generalization beyond merely data-driven methods. The effectiveness of PDE+ is validated through extensive experimental settings, demonstrating its superior performance compared to state-of-the-art methods. Our code is available at https://***/yuanyige/pde-add. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diffusion

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Enhancing Stance Detection on Social Media via Core Views Discovery 27

Enhancing Stance Detection on Social Media via Core Views Di...

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27th European Conference on Artificial Intelligence, ECai 2024

作者： Yan, Yu Shen, Yinghan Liu, Teli Jiang, Xuhui Yin, Dechun School of Information and Network Security People Public Security University of China China Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781643685489

Stance detection aims to identify the expressed attitude towards a target from the text, which is significant for learning public cognition from social media. The short and implicit nature of social media users' expressions potentially results in the stance understanding bias of the model. To address this problem, introducing external background information is helpful to mitigate these biases and enhance explainability. The core view, reflecting the motivations and reasons behind an individual's stance toward the target, can be summarized and extracted from collective tweets, which can serve as a reference for stance detection. In this study, we propose the Stance Detection via Core View Discovery (SD-CVM), where the core views are used for background information modeling. Specifically, we construct a joint classifier combining the semantic understanding of tweets and their relevant core views from the public. We utilize the Large Language Model (LLM) to extract core views with stances from tweets and use these core views as background references for tweets. To further optimize the tweet understanding, we develop the contrastive and rebalancing mechanism by incorporating stance supervision signals for training. Experiments on two representative datasets demonstrate the excellent performance of our method. © 2024 The Authors.

关键词： Tweets

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Rethinking the Evaluation of In-Context Learning for LLMs

Rethinking the Evaluation of In-Context Learning for LLMs

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

作者： Yu, Guoxin Liu, Lemao Yu, Mo Yu, Yue Ao, Xiang Institute of Computing Technology CAS Beijing100190 China Peng Cheng Laboratory Shenzhen China University of Chinese Academy of Sciences Beijing100049 China Wechat AI Tencent China Key Lab of AI Safety Chinese Academy of Sciences Beijing100094 China

ISBN: (纸本)9798891761643

In-context learning (ICL) has demonstrated excellent performance across various downstream NLP tasks, especially when synergized with powerful large language models (LLMs). Existing studies evaluate ICL methods primarily based on downstream task performance. This evaluation protocol overlooks the significant cost associated with the demonstration configuration process, i.e., tuning the demonstration as the ICL prompt. However, in this work, we point out that the evaluation protocol leads to unfair comparisons and potentially biased evaluation, because we surprisingly find the correlation between the configuration costs and task performance. Then we call for a two-dimensional evaluation paradigm that considers both of these aspects, facilitating a fairer comparison. Finally, based on our empirical finding that the optimized demonstration on one language model generalizes across language models of different sizes, we introduce a simple yet efficient strategy that can be applied to any ICL method as a plugin, yielding a better trade-off between the two dimensions according to the proposed evaluation paradigm. © 2024 Association for Computational Linguistics.

关键词： Demonstrations

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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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IEEE International Conference on Big Data

作者： Zongli Jiang Yirui Tan Guoxin Chen Fangda Guo Jinli Zhang Xiaolu Bai Computer Science and Technology Beijing University of Technology Beijing China Key Laboratory of AI Safety Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China Key Laboratory of AI Safety Institute of Computing Technology CAS Beijing China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

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.

关键词： Computational modeling Heuristic algorithms Organizations Big Data Benchmark testing Search problems Graph neural networks Data models Real-time systems

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Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System

arXiv

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arXiv 2024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

Recommender systems play a pivotal role in mitigating information overload in various fields. Nonetheless, the inherent openness of these systems introduces vulnerabilities, allowing attackers to insert fake users into the system’s training data to skew the exposure of certain items, known as poisoning attacks. Adversarial training has emerged as a notable defense mechanism against such poisoning attacks within recommender systems. Existing adversarial training methods apply perturbations of the same magnitude across all users to enhance system robustness against attacks. Yet, in reality, we find that attacks often affect only a subset of users who are vulnerable. These perturbations of indiscriminate magnitude make it difficult to balance effective protection for vulnerable users without degrading recommendation quality for those who are not affected. To address this issue, our research delves into understanding user vulnerability. Considering that poisoning attacks pollute the training data, we note that the higher degree to which a recommender system fits users’ training data correlates with an increased likelihood of users incorporating attack information, indicating their vulnerability. Leveraging these insights, we introduce the Vulnerability-aware Adversarial Training (VAT), designed to defend against poisoning attacks in recommender systems. VAT employs a novel vulnerability-aware function to estimate users’ vulnerability based on the degree to which the system fits them. Guided by this estimation, VAT applies perturbations of adaptive magnitude to each user, not only reducing the success ratio of attacks but also preserving, and potentially enhancing, the quality of recommendations. Comprehensive experiments confirm VAT’s Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning

arXiv

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arXiv 2024年

作者： Wang, Zixu Xu, Bingbing Yuan, Yige 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

Graph contrastive learning (GCL), standing as the dominant paradigm in the realm of graph pre-training, has yielded considerable progress. Nonetheless, its capacity for out-of-distribution (OOD) generalization has been relatively underexplored. In this work, we point out that the traditional optimization of InfoNCE in GCL restricts the cross-domain pairs only to be negative samples, which inevitably enlarges the distribution gap between different domains. This violates the requirement of domain invariance under OOD scenario and consequently impairs the model’s OOD generalization performance. To address this issue, we propose a novel strategy "Negative as Positive", where the most semantically similar cross-domain negative pairs are treated as positive during GCL. Our experimental results, spanning a wide array of datasets, confirm that this method substantially improves the OOD generalization performance of GCL. © 2024, CC BY.

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LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning Attacks

arXiv

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arXiv 2024年

Sequential recommender systems stand out for their ability to capture users’ dynamic interests and the patterns of item transitions. However, the inherent openness of sequential recommender systems renders them vulnerable to poisoning attacks, where fraudsters are injected into the training data to manipulate learned patterns. Traditional defense methods predominantly depend on predefined assumptions or rules extracted from specific known attacks, limiting their generalizability to unknown attacks. To solve the above problems, considering the rich open-world knowledge encapsulated in Large Language Models (LLMs), we attempt to introduce LLMs into defense methods to broaden the knowledge beyond limited known attacks. We propose LoRec, an innovative framework that employs LLM-Enhanced Calibration to strengthen the robustness of sequential Recommender systems against poisoning attacks. LoRec integrates an LLM-enhanced CalibraTor (LCT) that refines the training process of sequential recommender systems with knowledge derived from LLMs, applying a user-wise reweighting to diminish the impact of attacks. Incorporating LLMs’ open-world knowledge, the LCT effectively converts the limited, specific priors or rules into a more general pattern of fraudsters, offering improved defenses against poisons. Our comprehensive experiments validate that LoRec, as a general framework, significantly strengthens the robustness of sequential recommender systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Calibration

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