7 citations · 9 across the 13 of their papers we have counts for
18 papers
SEAD: A State-Based Perspective on Attack and Defense in Tool-Using Agents
Xinjie Shen, Junran Wang, Rongzhe Wei +1
Language-model agents increasingly use tools to act on external systems. Earlier actions can alter files, permissions, database records, or other state, making a later routine-look…
Making LLMs Truly Forget: Deep Unlearning by Searching, Selecting, and Severing Knowledge Paths
Jialu Wang, Peizhi Niu, Haoteng Yin +3
While an unlearned language model may no longer recall a fact directly, the fact often remains recoverable through multi-hop reasoning over related knowledge. Most existing unlearn…
What Must a World Model Distinguish for Planning?
Rongzhe Wei, Hans Hao-Hsun Hsu, Peizhi Niu +2
World models simulate the consequences of action candidates, but good planning need not preserve every physical distinction required for accurate prediction. We formalize this gap…
One Turn Too Late: Learning When to Intervene Against Multi-Turn Malicious Intent
Xinjie Shen, Rongzhe Wei, Peizhi Niu +6
Hidden malicious intent in multi-turn dialogue poses a growing threat to deployed large language models (LLMs). Rather than exposing a harmful objective in a single prompt, attacke…
Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching
Rongzhe Wei, Ge Shi, Min Cheng +5
Large Language Models (LLMs) have significantly advanced tool-augmented agents, enabling autonomous reasoning via API interactions. However, executing multi-step tasks within massi…
MoEEdit: Efficient and Routing-Stable Knowledge Editing for Mixture-of-Experts LLMs
Yupu Gu, Rongzhe Wei, Andy Zhu +1
Knowledge editing (KE) enables precise modifications to factual content in large language models (LLMs). Existing KE methods are largely designed for dense architectures, limiting…
GRIP: Algorithm-Agnostic Machine Unlearning for Mixture-of-Experts via Geometric Router Constraints
Andy Zhu, Rongzhe Wei, Yupu Gu +1
Machine unlearning in Mixture-of-Experts (MoE) large language models presents a critical yet under-explored challenge. Current unlearning methods applied to MoE architectures often…
The Trojan Knowledge: Bypassing Commercial LLM Guardrails via Harmless Prompt Weaving and Adaptive Tree Search
Rongzhe Wei, Peizhi Niu, Xinjie Shen +7
Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs. Existing approaches overwhelmingly operate within the p…
Towards Universal Debiasing for Language Models-based Tabular Data Generation
Tianchun Li, Tianci Liu, Xingchen Wang +4
Large language models (LLMs) have achieved promising results in tabular data generation. However, inherent historical biases in tabular datasets often cause LLMs to exacerbate fair…
Differentially Private Relational Learning with Entity-level Privacy Guarantees
Yinan Huang, Haoteng Yin, Eli Chien +2
Learning with relational and network-structured data is increasingly vital in sensitive domains where protecting the privacy of individual entities is paramount. Differential Priva…
Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness
Rongzhe Wei, Peizhi Niu, Hans Hao-Hsun Hsu +9
Machine unlearning techniques aim to mitigate unintended memorization in large language models (LLMs). However, existing approaches predominantly focus on the explicit removal of i…
Model Generalization on Text Attribute Graphs: Principles with Large Language Models
Haoyu Wang, Shikun Liu, Rongzhe Wei +1
Large language models (LLMs) have recently been introduced to graph learning, aiming to extend their zero-shot generalization success to tasks where labeled graph data is scarce. A…
Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning
Rongzhe Wei, Mufei Li, Mohsen Ghassemi +7
Large Language Models (LLMs) embed sensitive, human-generated data, prompting the need for unlearning methods. Although certified unlearning offers strong privacy guarantees, its r…
Privately Learning from Graphs with Applications in Fine-tuning Large Language Models
Haoteng Yin, Rongzhe Wei, Eli Chien +1
Graphs offer unique insights into relationships between entities, complementing data modalities like text and images and enabling AI models to extend their capabilities beyond trad…
Differentially Private Graph Diffusion with Applications in Personalized PageRanks
Rongzhe Wei, Eli Chien, Pan Li
Graph diffusion, which iteratively propagates real-valued substances among the graph, is used in numerous graph/network-involved applications. However, releasing diffusion vectors…
Guarding Multiple Secrets: Enhanced Summary Statistic Privacy for Data Sharing
Shuaiqi Wang, Rongzhe Wei, Mohsen Ghassemi +2
Data sharing enables critical advances in many research areas and business applications, but it may lead to inadvertent disclosure of sensitive summary statistics (e.g., means or q…
Learning Scalable Structural Representations for Link Prediction with Bloom Signatures
Tianyi Zhang, Haoteng Yin, Rongzhe Wei +2
Graph neural networks (GNNs) have shown great potential in learning on graphs, but they are known to perform sub-optimally on link prediction tasks. Existing GNNs are primarily des…
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models
Rongzhe Wei, Eleonora Kreačić, Haoyu Wang +4
Privacy concerns have led to a surge in the creation of synthetic datasets, with diffusion models emerging as a promising avenue. Although prior studies have performed empirical ev…