13 papers
PRO: Enabling Precise and Robust Text Watermark for Open-Source LLMs
Jiaqi Xue, Yifei Zhao, Mansour Al Ghanim +4
Text watermarking for large language models (LLMs) enables model owners to verify text origin and protect intellectual property. While watermarking methods for closed-source LLMs a…
DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
Jiaqi Xue, Mayank Kumar, Yuzhang Shang +5
Federated Learning (FL) enables collaborative model training across institutions without sharing raw data. However, gradient sharing still risks privacy leakage, such as gradient i…
VQEzy: An Open-Source Dataset for Parameter Initialization in Variational Quantum Eigensolvers
Chi Zhang, Mengxin Zheng, Qian Lou +2
Variational Quantum Eigensolvers (VQEs) are a leading class of noisy intermediate-scale quantum (NISQ) algorithms, whose performance is highly sensitive to parameter initialization…
DiffQ: Unified Parameter Initialization for Variational Quantum Algorithms via Diffusion Models
Chi Zhang, Mengxin Zheng, Qian Lou +1
Variational Quantum Algorithms (VQAs) are widely used in the noisy intermediate-scale quantum (NISQ) era, but their trainability and performance depend critically on initialization…
Factuality Beyond Coherence: Evaluating LLM Watermarking Methods for Medical Texts
Rochana Prih Hastuti, Rian Adam Rajagede, Mansour Al Ghanim +2
As large language models (LLMs) are adapted to sensitive domains such as medicine, their fluency raises safety risks, particularly regarding provenance and accountability. Watermar…
PIR-RAG: A System for Private Information Retrieval in Retrieval-Augmented Generation
Baiqiang Wang, Qian Lou, Mengxin Zheng +1
Retrieval-Augmented Generation (RAG) has become a foundational component of modern AI systems, yet it introduces significant privacy risks by exposing user queries to service provi…