18 citations · 90 across the 17 of their papers we have counts for
18 papers
Steering Black-Box Large Language Model Agents with Pitfall Anticipation World Model
Jiongxiao Wang, Han Ding, Youzhi Luo +4
Frontier black-box Large Language Model (LLM) agents are proficient at long-horizon reasoning and precise action generation. However, when it comes to a specific agentic task, they…
Generating Biomedical Fact-Checking Reports with RL-Enhanced Agentic Search
Jiongxiao Wang, Dingli Ma, Chaoqun Ni
Automated fact-checking is essential for ensuring the reliability of public health information, yet the biomedical domain poses unique challenges. Validating biomedical claims requ…
Reinforcement Learning for Self-Improving Agent with Skill Library
Jiongxiao Wang, Qiaojing Yan, Yawei Wang +6
Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt wh…
Robust Representation Consistency Model via Contrastive Denoising
Jiachen Lei, Julius Berner, Jiongxiao Wang +5
Robustness is essential for deep neural networks, especially in security-sensitive applications. To this end, randomized smoothing provides theoretical guarantees for certifying ro…
FATH: Authentication-based Test-time Defense against Indirect Prompt Injection Attacks
Jiongxiao Wang, Fangzhou Wu, Wendi Li +5
Large language models (LLMs) have been widely deployed as the backbone with additional tools and text information for real-world applications. However, integrating external informa…
Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset
Yingzi Ma, Jiongxiao Wang, Fei Wang +10
Machine unlearning has emerged as an effective strategy for forgetting specific information in the training data. However, with the increasing integration of visual data, privacy c…
Consistency Purification: Effective and Efficient Diffusion Purification towards Certified Robustness
Yiquan Li, Zhongzhu Chen, Kun Jin +3
Diffusion Purification, purifying noised images with diffusion models, has been widely used for enhancing certified robustness via randomized smoothing. However, existing framework…
Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors
Jiachen Sun, Changsheng Wang, Jiongxiao Wang +2
Large language models have become increasingly prominent, also signaling a shift towards multimodality as the next frontier in artificial intelligence, where their embeddings are h…
Mitigating Fine-tuning based Jailbreak Attack with Backdoor Enhanced Safety Alignment
Jiongxiao Wang, Jiazhao Li, Yiquan Li +7
Despite the general capabilities of Large Language Models (LLM), these models still request fine-tuning or adaptation with customized data when meeting specific business demands. H…
Preference Poisoning Attacks on Reward Model Learning
Junlin Wu, Jiongxiao Wang, Chaowei Xiao +3
Learning reward models from pairwise comparisons is a fundamental component in a number of domains, including autonomous control, conversational agents, and recommendation systems,…
Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations
Wenjie Mo, Jiashu Xu, Qin Liu +5
Existing studies in backdoor defense have predominantly focused on the training phase, overlooking the critical aspect of testing time defense. This gap becomes pronounced in the c…
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models
Jiongxiao Wang, Junlin Wu, Muhao Chen +2
Reinforcement Learning with Human Feedback (RLHF) is a methodology designed to align Large Language Models (LLMs) with human preferences, playing an important role in LLMs alignmen…
On the Exploitability of Instruction Tuning
Manli Shu, Jiongxiao Wang, Chen Zhu +3
Instruction tuning is an effective technique to align large language models (LLMs) with human intents. In this work, we investigate how an adversary can exploit instruction tuning…
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
Shengchao Liu, Jiongxiao Wang, Yijin Yang +4
Recent advancements in conversational large language models (LLMs), such as ChatGPT, have demonstrated remarkable promise in various domains, including drug discovery. However, exi…
Adversarial Demonstration Attacks on Large Language Models
Jiongxiao Wang, Zichen Liu, Keun Hee Park +5
With the emergence of more powerful large language models (LLMs), such as ChatGPT and GPT-4, in-context learning (ICL) has gained significant prominence in leveraging these models…
Defending against Adversarial Audio via Diffusion Model
Shutong Wu, Jiongxiao Wang, Wei Ping +2
Deep learning models have been widely used in commercial acoustic systems in recent years. However, adversarial audio examples can cause abnormal behaviors for those acoustic syste…
DensePure: Understanding Diffusion Models towards Adversarial Robustness
Chaowei Xiao, Zhongzhu Chen, Kun Jin +6
Diffusion models have been recently employed to improve certified robustness through the process of denoising. However, the theoretical understanding of why diffusion models are ab…
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
Ruize Gao, Jiongxiao Wang, Kaiwen Zhou +5
The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available. However, the high computational co…