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WAPITI: A Watermark for Finetuned Open-Source LLMs

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arxiv 2410.06467 v2 pith:IAAK5LJ4 submitted 2024-10-09 cs.CR

classification cs.CR
keywords modelsfine-tunedwatermarkingllmsmethodopen-sourcewatermarkattacks
verification ladder T0 review T1 audit T2 compute T3 formal
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Watermarking of large language models (LLMs) generation embeds an imperceptible statistical pattern within texts, making it algorithmically detectable. Watermarking is a promising method for addressing potential harm and biases from LLMs, as it enables traceability, accountability, and detection of manipulated content, helping to mitigate unintended consequences. However, for open-source models, watermarking faces two major challenges: (i) incompatibility with fine-tuned models, and (ii) vulnerability to fine-tuning attacks. In this work, we propose WAPITI, a new method that transfers watermarking from base models to fine-tuned models through parameter integration. To the best of our knowledge, we propose the first watermark for fine-tuned open-source LLMs that preserves their fine-tuned capabilities. Furthermore, our approach offers an effective defense against fine-tuning attacks. We test our method on various model architectures and watermarking strategies. Results demonstrate that our method can successfully inject watermarks and is highly compatible with fine-tuned models. Additionally, we offer an in-depth analysis of how parameter editing influences the watermark strength and overall capabilities of the resulting models.

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Cited by 3 Pith papers

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    A multifurcation reward model that scores all next-token candidates in one call makes inference-time safety scaling with tree search far more compute-efficient than best-of-N sampling.

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    AdaDecode speeds up LLM generation by predicting tokens at early layers when confidence is high, running the skipped layers in parallel, and verifying the output exactly matches standard decoding.

  3. Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning

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    Self-Error-Instruct clusters a model's math errors into types, synthesizes targeted practice data per type, and selects the best samples for fine-tuning, improving held-out math test accuracy.

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