351 citations · 434 across the 12 of their papers we have counts for
13 papers
Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation
Liuji Chen, Xiaofang Yang, Yuanzhuo Lu +6
Retrieval-Augmented Generation (RAG) systems improve the factual grounding of large language models (LLMs) but remain vulnerable to retrieval poisoning, where adversaries seed the…
SEEM: Exploiting Black-Box Text Attacks to Manipulate Tool Selection
Liuji Chen, Hao Gao, Jinghao Zhang +3
Tool learning has emerged as a powerful auxiliary mechanism that extends the capabilities of large language models (LLMs), enabling them to address complex tasks that demand real-t…
CoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendation
Yuting Liu, Jinghao Zhang, Yizhou Dang +5
Involving collaborative information in Large Language Models (LLMs) is a promising technique for adapting LLMs for recommendation. Existing methods achieve this by concatenating co…
Modality-Balanced Learning for Multimedia Recommendation
Jinghao Zhang, Guofan Liu, Qiang Liu +2
Many recommender models have been proposed to investigate how to incorporate multimodal content information into traditional collaborative filtering framework effectively. The use…
Interpretable Multimodal Out-of-context Detection with Soft Logic Regularization
Huanhuan Ma, Jinghao Zhang, Qiang Liu +2
The rapid spread of information through mobile devices and media has led to the widespread of false or deceptive news, causing significant concerns in society. Among different type…
Stealthy Attack on Large Language Model based Recommendation
Jinghao Zhang, Yuting Liu, Qiang Liu +3
Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS). However, while these systems have flourished, the…
Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models
Junfei Wu, Qiang Liu, Ding Wang +4
Object hallucination has been an Achilles' heel which hinders the broader applications of large vision-language models (LVLMs). Object hallucination refers to the phenomenon that t…
Decomposition Ascribed Synergistic Learning for Unified Image Restoration
Jinghao Zhang, Feng Zhao
Learning to restore multiple image degradations within a single model is quite beneficial for real-world applications. Nevertheless, existing works typically concentrate on regardi…
Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation
Jinghao Zhang, Qiang Liu, Shu Wu +1
Multimedia content is of predominance in the modern Web era. In real scenarios, multiple modalities reveal different aspects of item attributes and usually possess different import…
Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu +3
Recent years have witnessed growing interests in multimedia recommendation, which aims to predict whether a user will interact with an item with multimodal contents. Previous studi…
Mining Latent Structures for Multimedia Recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu +3
Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommen…
A Survey on Graph Structure Learning: Progress and Opportunities
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang +5
Graphs are widely used to describe real-world objects and their interactions. Graph Neural Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly sensit…
A Graph-based Relevance Matching Model for Ad-hoc Retrieval
Yufeng Zhang, Jinghao Zhang, Zeyu Cui +2
To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial. Recent deep learning models regard the task a…