1 citations · 2 across the 10 of their papers we have counts for
11 papers
AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science
An Luo, Jin Du, Xun Xian +12
Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) and artificial…
Membership Inference Attacks Against Fine-tuned Diffusion Language Models
Yuetian Chen, Kaiyuan Zhang, Yuntao Du +5
Diffusion Language Models (DLMs) represent a promising alternative to autoregressive language models, using bidirectional masked token prediction. Yet their susceptibility to priva…
Window-based Membership Inference Attacks Against Fine-tuned Large Language Models
Yuetian Chen, Yuntao Du, Kaiyuan Zhang +4
Most membership inference attacks (MIAs) against Large Language Models (LLMs) rely on global signals, like average loss, to identify training data. This approach, however, dilutes…
Can Agentic AI Match the Performance of Human Data Scientists?
An Luo, Jin Du, Fangqiao Tian +9
Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) have significa…
NetBurst: Event-Centric Forecasting of Bursty, Intermittent Time Series
Satyandra Guthula, Jaber Daneshamooz, Charles Fleming +3
Network operators monitor their infrastructure by collecting telemetry data such as packet counts, byte rates, or flow volumes, yet answering the questions that effective operation…
SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks
Kaiyuan Zhang, Siyuan Cheng, Hanxi Guo +8
Large language models (LLMs) have achieved remarkable success and are widely adopted for diverse applications. However, fine-tuning these models often involves private or sensitive…
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science
An Luo, Xun Xian, Jin Du +12
Large language models (LLMs) have advanced the automation of data science workflows. Yet it remains unclear whether they can critically leverage external domain knowledge as human…
An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems
Fangqiao Tian, An Luo, Jin Du +12
A multi-agent AI system (MAS) is composed of multiple autonomous agents that interact, exchange information, and make decisions based on internal generative models. Recent advances…
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains
Xun Xian, Ganghua Wang, Xuan Bi +5
Retrieval-Augmented Generation (RAG) has been empirically shown to enhance the performance of large language models (LLMs) in knowledge-intensive domains such as healthcare, financ…
RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees
Xun Xian, Ganghua Wang, Xuan Bi +4
Safeguarding intellectual property and preventing potential misuse of AI-generated images are of paramount importance. This paper introduces a robust and agile plug-and-play waterm…
Demystifying Poisoning Backdoor Attacks from a Statistical Perspective
Ganghua Wang, Xun Xian, Jayanth Srinivasa +4
The growing dependence on machine learning in real-world applications emphasizes the importance of understanding and ensuring its safety. Backdoor attacks pose a significant securi…