On the Reliability of Watermarks for Large Language Models
Authors: name: John Kirchenbauer, name: Jonas Geiping, name: Yuxin Wen, name: Manli Shu, name: Khalid Saifullah, name: Kezhi Kong, name: Kasun Fernando, name: Aniruddha Saha, name: Micah Goldblum, name: Tom Goldstein

Large language models (LLMs) are now deployed to everyday use and positioned
to produce large quantities of text in the coming decade. Machine-generated
text may displace human-written text on the internet and has the potential to
be used for malicious purposes, such as spearphishing attacks and social media
bots. Watermarking is a simple and effective strategy for mitigating such harms
by enabling the detection and documentation of LLM-generated text. Yet, a
crucial question remains: How reliable is watermarking in realistic settings in
the wild? There, watermarked text might be mixed with other text sources,
paraphrased by human writers or other language models, and used for
applications in a broad number of domains, both social and technical. In this
paper, we explore different detection schemes, quantify their power at
detecting watermarks, and determine how much machine-generated text needs to be
observed in each scenario to reliably detect the watermark. We especially
highlight our human study, where we investigate the reliability of watermarking
when faced with human paraphrasing. We compare watermark-based detection to
other detection strategies, finding overall that watermarking is a reliable
solution, especially because of its sample complexity - for all attacks we
consider, the watermark evidence compounds the more examples are given, and the
watermark is eventually detected.