17 citations · 20 across the 10 of their papers we have counts for
12 papers
Optimal Watermark Localization in Mixed-Source Large Language Model Texts
Jose H. Blanchet, T. Tony Cai, Xiang Li +3
Watermarking provides a principled way to authenticate text generated by large language models (LLMs). In practice, however, the final text may be mixed-source, with watermark evid…
Selective Disclosure Watermarking for Large Language Models
Xuyang Chen, Xiang Li, Yangxinyu Xie +1
Watermarking methods embed imperceptible and verifiable signals into text generated by large language models (LLMs). Existing approaches include zero-bit schemes for distinguishing…
Improving the Trade-off Between Watermark Strength and Speculative Sampling Efficiency for Language Models
Weiqing He, Xiang Li, Li Shen +2
Watermarking is a principled approach for tracing the provenance of large language model (LLM) outputs, but its deployment in practice is hindered by inference inefficiency. Specul…
Robust Spectral Watermark for Synthetic Tabular Data
Yizhou Zhao, Xiang Li, Peter Song +2
The rise of generative AI has enabled the production of high-fidelity synthetic tabular data across fields such as healthcare, finance, and public policy, raising growing concerns…
Optimal Detection for Language Watermarks with Pseudorandom Collision
T. Tony Cai, Xiang Li, Qi Long +2
Text watermarking plays a crucial role in ensuring the traceability and accountability of large language model (LLM) outputs and mitigating misuse. While promising, most existing m…
Mitigating Privacy-Utility Trade-off in Decentralized Federated Learning via -Differential Privacy
Xiang Li, Buxin Su, Chendi Wang +2
Differentially private (DP) decentralized Federated Learning (FL) allows local users to collaborate without sharing their data with a central server. However, accurately quantifyin…
On the Empirical Power of Goodness-of-Fit Tests in Watermark Detection
Weiqing He, Xiang Li, Tianqi Shang +3
Large language models (LLMs) raise concerns about content authenticity and integrity because they can generate human-like text at scale. Text watermarks, which embed detectable sta…
Optimal Estimation of Watermark Proportions in Hybrid AI-Human Texts
Xiang Li, Garrett Wen, Weiqing He +3
Text watermarks in large language models (LLMs) are an increasingly important tool for detecting synthetic text and distinguishing human-written content from LLM-generated text. Wh…
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know?
Xiang Li, Jiayi Xin, Qi Long +1
Accurate evaluation of large language models (LLMs) is crucial for understanding their capabilities and guiding their development. However, current evaluations often inconsistently…
Robust Detection of Watermarks for Large Language Models Under Human Edits
Xiang Li, Feng Ruan, Huiyuan Wang +2
Watermarking has offered an effective approach to distinguishing text generated by large language models (LLMs) from human-written text. However, the pervasive presence of human ed…
Debiasing Watermarks for Large Language Models via Maximal Coupling
Yangxinyu Xie, Xiang Li, Tanwi Mallick +2
Watermarking language models is essential for distinguishing between human and machine-generated text and thus maintaining the integrity and trustworthiness of digital communicatio…
A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules
Xiang Li, Feng Ruan, Huiyuan Wang +2
Since ChatGPT was introduced in November 2022, embedding (nearly) unnoticeable statistical signals into text generated by large language models (LLMs), also known as watermarking,…