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[2506.04462] Watermarking Degrades Alignment in Language Models: Analysis and Mitigation
Summary
This paper analyzes the impact of watermarking on the alignment of language models, revealing significant shifts in model behavior and proposing a method to mitigate these effects.
Why It Matters
As watermarking becomes a standard practice for ensuring the integrity of language model outputs, understanding its effects on model alignment is crucial. This research highlights potential safety and utility issues, offering solutions that can enhance the reliability of AI systems.
Key Takeaways
- Watermarking alters token probabilities, affecting model alignment.
- Two failure modes identified: guard attenuation and guard amplification.
- Alignment Resampling (AR) can restore alignment performance with minimal samples.
- The study provides the first empirical evidence of watermarking's impact on alignment.
- Understanding these interactions is vital for developing safer AI systems.
Computer Science > Computation and Language arXiv:2506.04462 (cs) [Submitted on 4 Jun 2025 (v1), last revised 23 Feb 2026 (this version, v4)] Title:Watermarking Degrades Alignment in Language Models: Analysis and Mitigation Authors:Apurv Verma, NhatHai Phan, Shubhendu Trivedi View a PDF of the paper titled Watermarking Degrades Alignment in Language Models: Analysis and Mitigation, by Apurv Verma and 2 other authors View PDF HTML (experimental) Abstract:Watermarking has become a practical tool for tracing language model outputs, but it modifies token probabilities at inference time, which were carefully tuned by alignment training. This creates a tension: how do watermark-induced shifts interact with the procedures intended to make models safe and useful? Experiments on several contemporary models and two representative watermarking schemes reveal that watermarking induces a nontrivial, patterned yet model-specific shift in alignment. We see two failure modes: guard attenuation, where models become more helpful but less safe, and guard amplification, where refusals become overly conservative. These effects persist even after controlling for perplexity degradation, pointing to alignment-specific distortions, not just quality loss. We address this with Alignment Resampling (AR), a procedure that samples multiple watermarked outputs and selects the most aligned response according to an external reward model. Using standard results on the expected maximum of Gaussian random va...
