11 citations · 27 across the 16 of their papers we have counts for
19 papers
PsyBer-Agent: A Psychology-Driven User Agent for Recommender Systems
Chong Zhang, Qijie Shen, Haichao Zhang +3
Evaluating recommender systems before deployment requires simulators that remain realistic over long, closed-loop interaction cycles. Current LLM-based agents generally function as…
SkinCLIP-VL: Consistency-Aware Vision-Language Learning for Multimodal Skin Cancer Diagnosis
Zhixiang Lu, Shijie Xu, Kaicheng Yan +6
The deployment of vision-language models (VLMs) in dermatology is hindered by the trilemma of high computational costs, extreme data scarcity, and the black-box nature of deep lear…
SAGE: Sustainable Agent-Guided Expert-tuning for Culturally Attuned Translation in Low-Resource Southeast Asia
Zhixiang Lu, Chong Zhang, Yulong Li +5
The vision of an inclusive World Wide Web is impeded by a severe linguistic divide, particularly for communities in low-resource regions of Southeast Asia. While large language mod…
StealthMark: Harmless and Stealthy Ownership Verification for Medical Segmentation via Uncertainty-Guided Backdoors
Qinkai Yu, Chong Zhang, Gaojie Jin +11
Annotating medical data for training AI models is often costly and limited due to the shortage of specialists with relevant clinical expertise. This challenge is further compounded…
Customized Retrieval-Augmented Generation with LLM for Debiasing Recommendation Unlearning
Haichao Zhang, Chong Zhang, Peiyu Hu +2
Modern recommender systems face a critical challenge in complying with privacy regulations like the 'right to be forgotten': removing a user's data without disrupting recommendatio…
Performance is not All You Need: Sustainability Considerations for Algorithms
Xiang Li, Chong Zhang, Hongpeng Wang +3
This work focuses on the high carbon emissions generated by deep learning model training, specifically addressing the core challenge of balancing algorithm performance and energy c…
Node-as-Agent: Graph Agentic Network
Minghao Guo, Xi Zhu, Qingyue Jiao +7
Graph Neural Networks (GNNs) have achieved remarkable success in graph-based learning by propagating information among neighbor nodes via predefined aggregation mechanisms. However…
Invisible Prompts, Visible Threats: Malicious Font Injection in External Resources for Large Language Models
Junjie Xiong, Changjia Zhu, Shuhang Lin +4
Large Language Models (LLMs) are increasingly equipped with capabilities of real-time web search and integrated with protocols like Model Context Protocol (MCP). This extension cou…
LLM-Enhanced Feature Engineering for Multi-Factor Electricity Price Predictions
Haochen Xue, Chenghao Liu, Chong Zhang +9
Accurately forecasting electricity price volatility is crucial for effective risk management and decision-making. Traditional forecasting models often fall short in capturing the c…
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation
Haochen Xue, Feilong Tang, Ming Hu +13
Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses. However, th…
Toward Equitable Access: Leveraging Crowdsourced Reviews to Investigate Public Perceptions of Health Resource Accessibility
Zhaoqian Xue, Guanhong Liu, Chong Zhang +7
Monitoring health resource disparities during public health crises is critical, yet traditional methods, like surveys, lack the requisite speed and spatial granularity. This study…
Counterfactual Explainable Incremental Prompt Attack Analysis on Large Language Models
Dong Shu, Mingyu Jin, Tianle Chen +2
This study sheds light on the imperative need to bolster safety and privacy measures in large language models (LLMs), such as GPT-4 and LLaMA-2, by identifying and mitigating their…
When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments
Chong Zhang, Xinyi Liu, Zhongmou Zhang +10
Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fund…
Better Sampling, towards Better End-to-end Small Object Detection
Zile Huang, Chong Zhang, Mingyu Jin +3
While deep learning-based general object detection has made significant strides in recent years, the effectiveness and efficiency of small object detection remain unsatisfactory. T…
Goal-guided Generative Prompt Injection Attack on Large Language Models
Chong Zhang, Mingyu Jin, Qinkai Yu +3
Current large language models (LLMs) provide a strong foundation for large-scale user-oriented natural language tasks. A large number of users can easily inject adversarial text or…
Knowledge Graph Large Language Model (KG-LLM) for Link Prediction
Dong Shu, Tianle Chen, Mingyu Jin +3
The task of multi-hop link prediction within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, as it requires the model to reason through and u…
Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities
Hua Tang, Chong Zhang, Mingyu Jin +5
Large language models (LLMs) have been applied in many fields and have developed rapidly in recent years. As a classic machine learning task, time series forecasting has recently b…
Health-LLM: Personalized Retrieval-Augmented Disease Prediction System
Qinkai Yu, Mingyu Jin, Dong Shu +8
Recent advancements in artificial intelligence (AI), especially large language models (LLMs), have significantly advanced healthcare applications and demonstrated potentials in int…
AttackEval: How to Evaluate the Effectiveness of Jailbreak Attacking on Large Language Models
Dong Shu, Chong Zhang, Mingyu Jin +3
Jailbreak attacks represent one of the most sophisticated threats to the security of large language models (LLMs). To deal with such risks, we introduce an innovative framework tha…