  Watermarking is a technical means to dissuade malfeasant usage of Large
Language Models. This paper proposes a novel watermarking scheme, so-called
WaterMax, that enjoys high detectability while sustaining the quality of the
generated text of the original LLM. Its new design leaves the LLM untouched (no
modification of the weights, logits, temperature, or sampling technique).
WaterMax balances robustness and complexity contrary to the watermarking
techniques of the literature inherently provoking a trade-off between quality
and robustness. Its performance is both theoretically proven and experimentally
validated. It outperforms all the SotA techniques under the most complete
benchmark suite.
