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MITA: Bridging the Gap between Model and Data for Test-time Adaptation

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arXiv 2024年

作者： Yuan, Yige Xu, Bingbing Xiao, Teng Hou, Liang 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 China Pennsylvania State University United States Kuaishou Technology China

Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. However, existing mainstream TTA methods, predominantly operating at batch level, often exhibit suboptimal performance in complex real-world scenarios, particularly when confronting outliers or mixed distributions. This phenomenon stems from a pronounced over-reliance on statistical patterns over the distinct characteristics of individual instances, resulting in a divergence between the distribution captured by the model and data characteristics. To address this challenge, we propose Meet-In-The-Middle based Test-Time Adaptation (MITA), which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions, thereby meeting in the middle. MITA pioneers a significant departure from traditional approaches that focus solely on aligning the model to the data, facilitating a more effective bridging of the gap between model’s distribution and data characteristics. Comprehensive experiments with MITA across three distinct scenarios (Outlier, Mixture, and Pure) demonstrate its superior performance over SOTA methods, highlighting its potential to significantly enhance generalizability in practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Data assimilation

Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop

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arXiv 2024年

作者： Zhou, Yuqi Dai, Sunhao Pang, Liang Wang, Gang Dong, Zhenhua Xu, Jun Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Beijing China CAS Key Laboratory of AI Safety Institute of Computing Technology CAS Beijing China Huawei Noah’s Ark Lab Shenzhen China

Recently, researchers have uncovered that neural retrieval models prefer ai-generated content (aiGC), called source bias [10, 40]. Compared to active search behavior, recommendation represents another important means of information acquisition, where users are more prone to source bias. Furthermore, delving into the recommendation scenario, as aiGC becomes integrated within the feedback loop involving users, data, and the recommender system, it progressively contaminates the candidate items, the user interaction history, and ultimately, the data used to train the recommendation models. How and to what extent the source bias affects the neural recommendation models within feedback loop remains unknown. In this study, we extend the investigation of source bias into the realm of recommender systems, specifically examining its impact across different phases of the feedback loop. We conceptualize the progression of aiGC integration into the recommendation content ecosystem in three distinct phases-HGC dominate, HGC-aiGC coexist, and aiGC dominance-each representing past, present, and future states, respectively. Through extensive experiments across three datasets from diverse domains, we demonstrate the prevalence of source bias and reveal a potential digital echo chamber with source bias amplification throughout the feedback loop. This trend risks creating a recommender ecosystem with limited information source, such as aiGC, being disproportionately recommended. To counteract this bias and prevent its escalation in the feedback loop, we introduce a black-box debiasing method that maintains model impartiality towards both HGC and aiGC. Our experimental results validate the effectiveness of the proposed debiasing method, confirming its potential to disrupt the feedback loop. © 2024, CC BY-NC-ND.

关键词： Recommender systems

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Context Graph

arXiv 2024年

作者： Xu, Chengjin Li, Muzhi Yang, Cehao Jiang, Xuhui Tang, Lumingyuan Qi, Yiyan Guo, Jian IDEA Research International Digital Economy Academy China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong CAS Key Laboratory of AI Safety Institute of Computing Technology CAS China

Knowledge Graphs (KGs) are foundational structures in many ai applications, representing entities and their interrelations through triples. However, triple-based KGs lack the contextual information of relational knowledge, like temporal dynamics and provenance details, which are crucial for comprehensive knowledge representation and effective reasoning. Instead, Context Graphs (CGs) expand upon the conventional structure by incorporating additional information such as time validity, geographic location, and source provenance. This integration provides a more nuanced and accurate understanding of knowledge, enabling KGs to offer richer insights and support more sophisticated reasoning processes. In this work, we first discuss the inherent limitations of triple-based KGs and introduce the concept of CGs, highlighting their advantages in knowledge representation and reasoning. We then present a context graph reasoning CGR3 paradigm that leverages large language models (LLMs) to retrieve candidate entities and related contexts, rank them based on the retrieved information, and reason whether sufficient information has been obtained to answer a query. Our experimental results demonstrate that CGR3 significantly improves performance on KG completion (KGC) and KG question answering (KGQA) tasks, validating the effectiveness of incorporating contextual information on KG representation and reasoning. © 2024, CC BY-NC-ND.

关键词： Knowledge graph

F2GNN: An Adaptive Filter with Feature Segmentation for Graph-Based Fraud Detection

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F2GNN: An Adaptive Filter with Feature Segmentation for Grap...

International Conference on Acoustics, Speech, and Signal Processing (IcasSP)

作者： Guanghui Hu Yang Liu Qing He Xiang Ao Henan Institute of Advanced Technology Zhengzhou University Zhengzhou P.R. China Key Laboratory of AI Safety & Security Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China CASMINO Ltd. Suzhou China

Graph Neural Networks (GNNs) have received remarkable success in identifying fraudulent activities on graphs. Most approaches leverage the full user feature together and aggregate the messages from its neighbors by a graph filter. However, due to the adversarial activities like the camouflage of fraudsters, most dimensions of fraudsters' features resemble normal users, and modeling the features as a whole cannot fully explore the small-portion fraudulent features. In this paper, we attempt to segment the user features and apply adaptive graph filters on each segmentation for better modeling of fraudulent features. We propose an adaptive filter with feature segmentation (shortened as F 2 GNN) to alleviate these challenges. Experimental results on two real-world datasets demonstrate that F 2 GNN outperforms state-of-the-art baselines for graph-based fraud detection. In addition, the adaptive filter with feature segmentation can effectively address the class imbalance problem in the task of fraud detection.

关键词：

The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse

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arXiv 2024年

作者： Yang, Wanli Sun, Fei Ma, Xinyu Liu, Xun Yin, Dawei Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China

Although model editing has shown promise in revising knowledge in Large Language Models (LLMs), its impact on the inherent capabilities of LLMs is often overlooked. In this work, we reveal a critical phenomenon: even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks. However, benchmarking LLMs after each edit, while necessary to prevent such collapses, is impractically time-consuming and resource-intensive. To mitigate this, we propose using perplexity as a surrogate metric, validated by extensive experiments demonstrating changes in an edited model’s perplexity are strongly correlated with its downstream task performances. We further conduct an in-depth study on sequential editing, a practical setting for real-world scenarios, across various editing methods and LLMs, focusing on hard cases from our previous single edit studies. The results indicate that nearly all examined editing methods result in model collapse after only few edits. To facilitate further research, we have utilized GPT-3.5 to develop a new dataset, HardEdit, based on those hard cases. This dataset aims to establish the foundation for pioneering research in reliable model editing and the mechanisms underlying editing-induced model collapse. We hope this work can draw the community’s attention to the potential risks inherent in model editing practices1 Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Query-centric Audio-Visual Cognition Network for Moment Retrieval, Segmentation and Step-Captioning

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arXiv 2024年

作者： Tu, Yunbin Li, Liang Su, Li Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of AI Safety of CAS Institute of Computing Technology Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Video has emerged as a favored multimedia format on the internet. To better gain video contents, a new topic HIREST is presented, including video retrieval, moment retrieval, moment segmentation, and step-captioning. The pioneering work chooses the pre-trained CLIP-based model for video retrieval, and leverages it as a feature extractor for other three challenging tasks solved in a multi-task learning paradigm. Nevertheless, this work struggles to learn the comprehensive cognition of user-preferred content, due to disregarding the hierarchies and association relations across modalities. In this paper, guided by the shallow-to-deep principle, we propose a query-centric audio-visual cognition (QUAG) network to construct a reliable multi-modal representation for moment retrieval, segmentation and step-captioning. Specifically, we first design the modality-synergistic perception to obtain rich audio-visual content, by modeling global contrastive alignment and local fine-grained interaction between visual and audio modalities. Then, we devise the query-centric cognition that uses the deep-level query to perform the temporal-channel filtration on the shallow-level audio-visual representation. This can cognize user-preferred content and thus attain a query-centric audio-visual representation for three tasks. Extensive experiments show QUAG achieves the SOTA results on HIREST. Further, we test QUAG on the query-based video summarization task and verify its good generalization. The code is available at https://***/tuyunbin/QUAG. © 2024, CC BY.

关键词： Multimedia systems

SecMdp: Towards Privacy-Preserving Multimodal Deep Learning in End-Edge-Cloud

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SecMdp: Towards Privacy-Preserving Multimodal Deep Learning ...

International Conference on Data Engineering

作者： Zhao Bai Mingyue Wang Fangda Guo Yu Guo Chengjun Cai Rongfang Bie Xiaohua Jia Beijing Normal University Harbin Institute of Technology CAS Key Laboratory of AI Safety & Security Institute of Computing Technology Chinese Academy of Sciences City University of Hong Kong Dongguan Research Institute City University of Hong Kong

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Multimodal deep learning technologies have advanced significantly, which brings extensive applications in diverse fields. The substantial computational demands of training and prediction in multimodal deep learning have made the End-Edge-Cloud (EEC) framework popular. It is essential to protect multimodal data and model privacy in such a framework. However, traditional cryptographic methods, though secure for data and models at edge nodes, cause efficiency limitations. In this paper, we propose SecMdp, an SGX-assisted secure computational framework for multimodal data in the EEC architecture. Edge nodes are equipped with the trusted execution environment (e.g., Intel SGX) to run multimodal algorithms. Additionally, to address the side-channel attacks of SGX, we present an enhanced PathORAM algorithm, MM_PathORAM, for the multimodal training and prediction processes, which are tailored for multimodal deep learning scenarios. It accelerates multimodal data access while protecting data privacy and model security. Experimental evaluation supports the effectiveness of our design in preserving edge computing efficiency. It demonstrates negligible impact on the speed of multimodal data loading, the configuration of model parameters during training, or the accuracy of predictions.

关键词： Deep learning Training Data privacy Computational modeling Loading Computer architecture Side-channel attacks

STRUEDIT: Structured Outputs Enable the Fast and Accurate Knowledge Editing for Large Language Models

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arXiv 2024年

作者： Bi, Baolong Liu, Shenghua Wang, Yiwei Mei, Lingrui Gao, Hongcheng Fang, Junfeng Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China University of California Los Angeles United States Hefei China

As the modern tool of choice for question answering, large language models (LLMs) are expected to deliver answers with up-to-date knowledge. To achieve such ideal question-answering systems, locating and then editing outdated knowledge in the natural language outputs is a general target of popular knowledge editing methods. However, this target is challenging, as both identifying which tokens to edit in the reasoning steps and ensuring the coherence of the revised reasoning chain are difficult tasks. We argue that these challenges stem from the unstructured nature of natural language outputs. To address the above challenges, we propose Structural Editing (STRUEDIT), an improved baseline for knowledge editing. We first prompt LLMs to produce structured outputs consisting of reasoning triplets. Then, STRUEDIT removes any potentially outdated knowledge and efficiently refills the structured outputs with up-to-date information in a single step. Experimental results show that STRUEDIT consistently delivers the highest accuracy with lowest latency compared with other knowledge editing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

SACH: Significant-Attributed Community Search in Heterogeneous Information Networks

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

International Conference on Data Engineering

作者： Yanghao Liu Fangda Guo Bingbing Xu Peng Bao Huawei Shen Xueqi Cheng 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 Beijing Jiaotong University Beijing China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

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.

关键词： Semantics Search problems Data engineering Indexes