Baselines for Identifying Watermarked Large Language Models
This addresses the need for transparency and accountability in AI by providing tools to identify watermarked LLMs, though it is incremental as it builds on existing watermarking concepts.
The paper tackles the problem of detecting watermarking schemes in closed-source large language models by introducing baseline algorithms that analyze output token and logit distributions, showing that watermarked models produce identifiable divergences from standard ones.
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.