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报告信息 主题 ：A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules 嘉宾 ：苏炜杰 地点 ：腾讯会议：782-711-380（或点击阅读原文） 时间 ：北京时间2024年05月25日（周六）21:00 报告摘要 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 talk, we will 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 starts by selecting a pivotal statistic of the text and a secret key -- provided by the LLM to the verifier -- to enable controlling the false positive rate (the
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