  Potential harms of Large Language Models such as mass misinformation and
plagiarism can be partially mitigated if there exists a reliable way to detect
machine generated text. In this paper, we propose a new watermarking method to
detect machine-generated texts. Our method embeds a unique pattern within the
generated text, ensuring that while the content remains coherent and natural to
human readers, it carries distinct markers that can be identified
algorithmically. Specifically, we intervene with the token sampling process in
a way which enables us to trace back our token choices during the detection
phase. We show how watermarking affects textual quality and compare our
proposed method with a state-of-the-art watermarking method in terms of
robustness and detectability. Through extensive experiments, we demonstrate the
effectiveness of our watermarking scheme in distinguishing between watermarked
and non-watermarked text, achieving high detection rates while maintaining
textual quality.
