Can you Finetune your Binoculars? Embedding Text Watermarks into the Weights of Large Language Models
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909572122279936 |
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| author | Elhassan, Fay Ajroldi, Niccolò Orvieto, Antonio Geiping, Jonas |
| author_facet | Elhassan, Fay Ajroldi, Niccolò Orvieto, Antonio Geiping, Jonas |
| contents | The indistinguishability of AI-generated content from human text raises challenges in transparency and accountability. While several methods exist to watermark models behind APIs, embedding watermark strategies directly into model weights that are later reflected in the outputs of the model is challenging. In this study we propose a strategy to finetune a pair of low-rank adapters of a model, one serving as the text-generating model, and the other as the detector, so that a subtle watermark is embedded into the text generated by the first model and simultaneously optimized for detectability by the second. In this way, the watermarking strategy is fully learned end-to-end. This process imposes an optimization challenge, as balancing watermark robustness, naturalness, and task performance requires trade-offs. We discuss strategies on how to optimize this min-max objective and present results showing the effect of this modification to instruction finetuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06446 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can you Finetune your Binoculars? Embedding Text Watermarks into the Weights of Large Language Models Elhassan, Fay Ajroldi, Niccolò Orvieto, Antonio Geiping, Jonas Machine Learning Artificial Intelligence The indistinguishability of AI-generated content from human text raises challenges in transparency and accountability. While several methods exist to watermark models behind APIs, embedding watermark strategies directly into model weights that are later reflected in the outputs of the model is challenging. In this study we propose a strategy to finetune a pair of low-rank adapters of a model, one serving as the text-generating model, and the other as the detector, so that a subtle watermark is embedded into the text generated by the first model and simultaneously optimized for detectability by the second. In this way, the watermarking strategy is fully learned end-to-end. This process imposes an optimization challenge, as balancing watermark robustness, naturalness, and task performance requires trade-offs. We discuss strategies on how to optimize this min-max objective and present results showing the effect of this modification to instruction finetuning. |
| title | Can you Finetune your Binoculars? Embedding Text Watermarks into the Weights of Large Language Models |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.06446 |