Abuse of large language models reveals high risks as large language models
are being deployed at an astonishing speed. It is important to protect the
model weights to avoid malicious usage that violates licenses of open-source
large language models. This paper proposes a novel watermarking strategy that
plants watermarks in the quantization process of large language models without
pre-defined triggers during inference. The watermark works when the model is
used in the fp32 mode and remains hidden when the model is quantized to int8,
in this way, the users can only inference the model without further supervised
fine-tuning of the model. We successfully plant the watermark into open-source
large language model weights including GPT-Neo and LLaMA. We hope our proposed
method can provide a potential direction for protecting model weights in the
era of large language model applications.

这篇论文提出了一种新颖的水印策略，通过在大型语言模型的量化过程中植入水印来保护模型权重，以避免恶意使用违反开源大型语言模型的许可证。该水印只在使用 fp32 模式的模型中起作用，并在将模型量化为 int8 时保持隐藏，使用户只能推理模型而无法进一步对模型进行监督微调。我们成功地将水印嵌入开源大型语言模型的权重中，包括 GPT-Neo 和 LLaMA。希望我们提出的方法能够为保护大型语言模型权重在大型语言模型应用时提供潜在方向。