GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick

• URL: http://arxiv.org/abs/2402.12948v3
• Date: Tue, 28 May 2024 04:58:41 GMT
• Title: GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick
• Authors: Jiayi Fu, Xuandong Zhao, Ruihan Yang, Yuansen Zhang, Jiangjie Chen, Yanghua Xiao,
• Abstract summary: 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. We propose a new type of GM watermark, the Logits-Addition watermark, and its three variants, specifically designed to enhance diversity.
• Score: 50.35069175236422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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