Baselines for Identifying Watermarked Large Language Models
Leonard Tang Gavin Uberti Tom Shlomi
We consider the emerging problem of identifying the presence and use of
watermarking schemes in widely used, publicly hosted, closed source large
language models (LLMs). We introduce a suite of baseline algorithms for
identifying watermarks in LLMs that rely on analyzing distributions of output
tokens and logits generated by watermarked and unmarked LLMs. Notably,
watermarked LLMs tend to produce distributions that diverge qualitatively and
identifiably from standard models. Furthermore, we investigate the
identifiability of watermarks at varying strengths and consider the tradeoffs
of each of our identification mechanisms with respect to watermarking scenario.
Along the way, we formalize the specific problem of identifying watermarks in
LLMs, as well as LLM watermarks and watermark detection in general, providing a
framework and foundations for studying them.