LLM Watermarking Using Mixtures and Statistical-to-Computational Gaps

Large Language Models (LLMs) have emerged as a powerful technology for generating human-like text [3, 20]. On one side, an LLM performs well if it produces text that closely resembles human writing. On the other side, the use of high-performance LLMs also bring undesirable consequences such as the spread of misinformation [19], misuse in education [15, 20], and data pollution [17, 18]. In this context, there is an urge to develop methods to distinguish human and AI generated text to mitigate those outcomes. One prominent technique is the so-called watermarking approach in which the goal is to embed a detectable signal in the text generated by the LLM.