  The task of discerning between generated and natural texts is increasingly
challenging. In this context, watermarking emerges as a promising technique for
ascribing generated text to a specific model. It alters the sampling generation
process so as to leave an invisible trace in the generated output, facilitating
later detection. This research consolidates watermarks for large language
models based on three theoretical and empirical considerations. First, we
introduce new statistical tests that offer robust theoretical guarantees which
remain valid even at low false-positive rates (less than 10$^{\text{-6}}$).
Second, we compare the effectiveness of watermarks using classical benchmarks
in the field of natural language processing, gaining insights into their
real-world applicability. Third, we develop advanced detection schemes for
scenarios where access to the LLM is available, as well as multi-bit
watermarking.
