1 citations · 1 across the 9 of their papers we have counts for
10 papers
What to Remember, What to Reveal: Privacy-Aware Memory for Conversational Agents
Wenjie Wang, Wenhe Si, Xinyue Xu +1
Long-term memory enables personalized conversational agents to retain user information across sessions. However, existing memory architectures primarily optimize for utility while…
From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space
Yue Xu, Yutao Sun, Yihao Liu +7
Long-term user memory is essential for personalized conversational agents, yet many memory systems still expose memory through passive retrieval interfaces, making the model a cons…
EVA: Editing for Versatile Alignment against Jailbreaks
Yi Wang, Hongye Qiu, Yue Xu +4
Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated impressive capabilities but remain vulnerable to jailbreaking attacks, where adversaries exploit te…
Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions
Yue Xu, Qian Chen, Zizhan Ma +5
Large language models have enabled agentic systems that reason, plan, and interact with tools and environments to accomplish complex tasks. As these agents operate over extended in…
From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship
Yue Xu, Wenjie Wang
Ensuring the trustworthiness of Multimodal Large Language Models (MLLMs) requires rigorous evaluation of gender bias, particularly in socially sensitive domains. Existing benchmark…
DR.GAP: Mitigating Bias in Large Language Models using Gender-Aware Prompting with Decoupled Reasoning
Hongye Qiu, Yue Xu, Yi Wang +2
Large Language Models (LLMs) exhibit strong natural language understanding capabilities but also inherit and amplify societal biases, particularly gender bias, raising fairness con…
Auto-Search and Refinement: An Automated Framework for Gender Bias Mitigation in Large Language Models
Yue Xu, Chengyan Fu, Li Xiong +2
Pre-training large language models (LLMs) on vast text corpora enhances natural language processing capabilities but risks encoding social biases, particularly gender bias. While p…
: A Comprehensive Study on Jailbreak Attacks and Defenses for Multimodal Large Language Models
Fenghua Weng, Yue Xu, Chengyan Fu +1
As deep learning advances, Large Language Models (LLMs) and their multimodal counterparts, Multimodal Large Language Models (MLLMs), have shown exceptional performance in many real…
Cross-modality Information Check for Detecting Jailbreaking in Multimodal Large Language Models
Yue Xu, Xiuyuan Qi, Zhan Qin +1
Multimodal Large Language Models (MLLMs) extend the capacity of LLMs to understand multimodal information comprehensively, achieving remarkable performance in many vision-centric t…
: Natural and Universal Adversarial Attacks on Prompt-based Language Models
Yue Xu, Wenjie Wang
Prompt-based learning is a new language model training paradigm that adapts the Pre-trained Language Models (PLMs) to downstream tasks, which revitalizes the performance benchmarks…