提出在线水印检测新方法，可实时判断AI生成文本
Efficient Online LLM Watermark Detection via Rao-Blackwellized E-Processes
- 用递归更新的统计方法，逐个词判断是否为AI生成
- 在真实数据上实现高效检测，错误率受严格控制
- 适合需要即时判断的场景，如内容审核系统
随着大语言模型广泛应用，区分人工智能生成文本与人工撰写内容的可靠、高效机制变得至关重要。统计水印已成为有前景的解决方案，但现有方法多为固定时长检测，无法支持流式生成中的有效提前停止。本文提出基于拉奥-布莱克韦尔化e过程的高效在线水印检测框架，实现任意时刻有效的推断，可在不存储完整历史的情况下递归更新每个词的证据。特别地，针对Gumbel-max水印，将原始的词级依赖性检验问题转化为具有显式零分布的枢轴序贯检验问题。理论上证明了在任意可选停止条件下，第一类错误率可控，并建立了在存在水印情况下的正渐近对数增长，表明停止规则具有一致性。模拟与真实大模型生成文本实验均验证了该方法在保证严格任意时刻有效性前提下的高效在线检测能力。
原文摘要 · Abstract (English)
As large language models (LLMs) are increasingly deployed, reliable and efficient mechanisms for distinguishing AI-generated text from human-written content have become essential. Statistical watermarking has emerged as a promising solution, yet most existing methods are typically fixed-horizon procedures, precluding valid early stopping in streaming generation. In this paper, we develop an efficient online watermark detection framework with anytime-valid inference based on Rao-Blackwellized e-processes, enabling recursive token-level evidence updates without storing the full history. In particular, we instantiate the framework for the Gumbel-max watermark and reduce the original token-level dependence testing problem to a pivot-induced sequential testing problem with an explicit null distribution. Theoretically, we prove anytime-valid Type I error control under arbitrary optional stopping and establish positive asymptotic log-growth under watermarking, implying consistency of the proposed stopping rules. Simulations and experiments on real LLM-generated text demonstrate efficient online detection with rigorous anytime-valid guarantees.
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