4 citations · 10 across the 11 of their papers we have counts for
11 papers
RobustMask: Certified Robustness against Adversarial Neural Ranking Attack via Randomized Masking
Jiawei Liu, Zhuo Chen, Rui Zhu +4
Neural ranking models have achieved remarkable progress and are now widely deployed in real-world applications such as Retrieval-Augmented Generation (RAG). However, like other neu…
Modality Equilibrium Matters: Minor-Modality-Aware Adaptive Alternating for Cross-Modal Memory Enhancement
Xiang Shi, Rui Zhang, Jiawei Liu +3
Multimodal fusion is susceptible to modality imbalance, where dominant modalities overshadow weak ones, easily leading to biased learning and suboptimal fusion, especially for inco…
Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models
Yuyang Gong, Zhuo Chen, Jiawei Liu +5
Retrieval-Augmented Generation (RAG) systems based on Large Language Models (LLMs) have become essential for tasks such as question answering and content generation. However, their…
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models
Zhuo Chen, Yuyang Gong, Jiawei Liu +6
Retrieval-Augmented Generation (RAG) enriches LLMs by dynamically retrieving external knowledge, reducing hallucinations and satisfying real-time information needs. While existing…
PersonaMark: Personalized LLM watermarking for model protection and user attribution
Yuehan Zhang, Peizhuo Lv, Yinpeng Liu +5
The rapid advancement of customized Large Language Models (LLMs) offers considerable convenience. However, it also intensifies concerns regarding the protection of copyright/confid…
Every Part Matters: Integrity Verification of Scientific Figures Based on Multimodal Large Language Models
Xiang Shi, Jiawei Liu, Yinpeng Liu +2
This paper tackles a key issue in the interpretation of scientific figures: the fine-grained alignment of text and figures. It advances beyond prior research that primarily dealt w…
Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models
Zhuo Chen, Jiawei Liu, Haotan Liu +4
Retrieval-Augmented Generation (RAG) is applied to solve hallucination problems and real-time constraints of large language models, but it also induces vulnerabilities against retr…
Enhance Robustness of Language Models Against Variation Attack through Graph Integration
Zi Xiong, Lizhi Qing, Yangyang Kang +5
The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes. However, these models' vulnerability to adv…
From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications
Yongqiang Ma, Lizhi Qing, Jiawei Liu +5
Evaluating large language models (LLMs) is fundamental, particularly in the context of practical applications. Conventional evaluation methods, typically designed primarily for LLM…
Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning
Yinpeng Liu, Jiawei Liu, Xiang Shi +3
Demonstration ordering, which is an important strategy for in-context learning (ICL), can significantly affects the performance of large language models (LLMs). However, most of th…
Know Where to Go: Make LLM a Relevant, Responsible, and Trustworthy Searcher
Xiang Shi, Jiawei Liu, Yinpeng Liu +2
The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches. However, challenges arise in validating the reli…