A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules

arXiv:2404.01245v1 Announce Type: cross
Abstract: Since ChatGPT was introduced in November 2022, embedding (nearly) unnoticeable statistical signals into text generated by large language models (LLMs), also known as watermarking, has been used as a principled approach to provable detection of LLM-generated text from its human-written counterpart. In this paper, we introduce a general and flexible framework for reasoning about the statistical efficiency of watermarks and designing powerful detection rules. Inspired by the hypothesis testing formulation of watermark detection, our framework …

abstract arxiv chatgpt cs.cl cs.cr cs.lg detection efficiency embedding framework generated human language language models large language large language models llm llms math.st pivot rules statistical stat.ml stat.th text type watermarking watermarks