  Large language models (LLMs) excellently generate human-like text, but also
raise concerns about misuse in fake news and academic dishonesty.
Decoding-based watermark, particularly the GumbelMax-trick-based watermark(GM
watermark), is a standout solution for safeguarding machine-generated texts due
to its notable detectability. However, GM watermark encounters a major
challenge with generation diversity, always yielding identical outputs for the
same prompt, negatively impacting generation diversity and user experience. To
overcome this limitation, we propose a new type of GM watermark, the
Logits-Addition watermark, and its three variants, specifically designed to
enhance diversity. Among these, the GumbelSoft watermark (a softmax variant of
the Logits-Addition watermark) demonstrates superior performance in high
diversity settings, with its AUROC score outperforming those of the two
alternative variants by 0.1 to 0.3 and surpassing other decoding-based
watermarking methods by a minimum of 0.1.
