5 papers
Watermark Robustness and Radioactivity May Be at Odds in Federated Learning
Leixu Huang, Zedian Shao, Teodora Baluta
Federated learning (FL) enables fine-tuning large language models (LLMs) across distributed data sources. As these sources increasingly include LLM-generated text, provenance track…
Taming Variability: Randomized and Bootstrapped Conformal Risk Control for LLMs
Lingyou Pang, Lei Huang, Jianyu Lin +3
We transform the randomness of LLMs into precise assurances using an actuator at the API interface that applies a user-defined risk constraint in finite samples via Conformal Risk…
Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty
Lingyou Pang, Lei Huang, Jianyu Lin +4
Deploying black-box LLMs requires managing uncertainty in the absence of token-level probability or true labels. We propose introducing an unsupervised conformal inference framewor…
TaxAgent: How Large Language Model Designs Fiscal Policy
Jizhou Wang, Xiaodan Fang, Lei Huang +1
Economic inequality is a global challenge, intensifying disparities in education, healthcare, and social stability. Traditional systems like the U.S. federal income tax reduce ineq…
Verification of Bit-Flip Attacks against Quantized Neural Networks
Yedi Zhang, Lei Huang, Pengfei Gao +3
In the rapidly evolving landscape of neural network security, the resilience of neural networks against bit-flip attacks (i.e., an attacker maliciously flips an extremely small amo…