Abstract
With the wide application of Large Language Models (LLMs), protecting the copyright of generated content and preventing its misuse becomes important. This paper proposes a temperature-based watermark embedding algorithm that embeds watermarks in text using the Softmax function and polynomial sampling techniques. Meanwhile, this paper also discusses a watermark detection technique based on statistical testing, which can effectively identify and verify watermarks embedded in text. By applying these techniques to different LLMs and computing environments, including OPT series, Llama series, BLOOM series and GPT-2, this paper analyses the scenarios, evaluates the key parameters in the algorithms and proposes solutions to ensure the integration of watermarks without compromising on the performance of the model or the naturalness of the generated text.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. U23A20307, No. 62272118). We extend our gratitude to the Foundation for their financial support, our collaborators and team members for their contributions, and the editors and reviewers for their valuable feedback.
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Chen, W., Zhang, Z., Ren, H., Ye, PG., Li, Z., Huang, S. (2025). Temperature-Based Watermarking and Detection for Large Language Models. In: Zhu, T., Li, J., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2024. Lecture Notes in Computer Science, vol 15251. Springer, Singapore. https://doi.org/10.1007/978-981-96-1525-4_18
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DOI: https://doi.org/10.1007/978-981-96-1525-4_18
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