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cs.LG2025
MarkTune: Improving the Quality-Detectability Trade-off in Model-Embedded LLM Watermarking
Yizhou Zhao, Zhiwei Steven Wu, Adam Block
Language model watermarking schemes fall into two broad categories: inference-time methods, which modify the decoding process at generation time, and model-embedded methods, which…
cs.LG2025
Small Loss Bounds for Online Learning Separated Function Classes: A Gaussian Process Perspective
Adam Block, Abhishek Shetty
In order to develop practical and efficient algorithms while circumventing overly pessimistic computational lower bounds, recent work has been interested in developing oracle-effic…
cs.CR2025
GaussMark: A Practical Approach for Structural Watermarking of Language Models
Adam Block, Ayush Sekhari, Alexander Rakhlin
Recent advances in Large Language Models (LLMs) have led to significant improvements in natural language processing tasks, but their ability to generate human-quality text raises s…