Baselines for Identifying Watermarked Large Language Models

Generated Text Detection Via Statistical Discrepancies Recent methods such as DetectGPT and GPTZero distinguish We consider the emerging problem of identifying between machine-generated and human-written text the presence and use of watermarking schemes by analyzing their statistical discrepancies (Tian, 2023; in widely used, publicly hosted, closed source Mitchell et al., 2023). DetectGPT compares the log probability large language models (LLMs). We introduce a computed by a model on unperturbed text and perturbed suite of baseline algorithms for identifying watermarks variations, leveraging the observation that text sampled from in LLMs that rely on analyzing distributions a LLM generally occupy negative curvature regions of the of output tokens and logits generated by model's log probability function. GPTZero instead uses watermarked and unmarked LLMs. Notably, watermarked perplexity and burstiness to distinguish human from machine LLMs tend to produce distributions text, with lower perplexity and burstiness indicating that diverge qualitatively and identifiably from a greater likelihood of machine-generated text.