TECH Signal 392
Author critiques watermarking schemes for AI-generated text, arguing they degrade quality and rely on secret keys
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The author argues that watermarking AI-generated text can slightly reduce quality and depends on secret keys held by providers, preventing public verification.
Engineers building LLM-based tools must consider whether the slight quality loss from watermarking is acceptable for their use case. Because the watermark cannot be inspected without the provider’s secret key, users lack a transparent way to verify authenticity, which may affect trust and compliance.
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Watermarking introduces secret-key dependent randomness that may slightly degrade the quality of generated prose.
Providers such as Anthropic’s Claude and Google’s Gemini hold the secret keys, making the watermarks unverifiable by end-users.
Watermarking is ineffective for exact text like programming code, limiting its usefulness to non-critical elements such as comments.
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The follow-up piece revisits the earlier criticism of Anthropic’s watermark approach, noting that large language models do not simply pick the highest-probability token at each step. Instead they apply a temperature setting that adds weighted randomness to improve output quality. The author explains that watermarking schemes replace this model-driven randomness with a deterministic pattern that is only predictable with a secret key.
Because the watermark uses predictable randomness for a purpose unrelated to improving prose, the author believes it inevitably makes the output at least slightly worse, akin to over-smoothing an image by removing all noise. Even if the temperature is left unchanged, swapping the source of randomness for a secret-key pattern can reduce the natural variability that contributes to fluent, pleasant text. This reduction in variability can make the text feel less natural and more mechanical.
The author objects to the secrecy of the watermark, pointing out that the schemes depend on keys held solely by the LLM providers, so anyone receiving the text cannot independently check for the mark. This lack of public verifiability contrasts with a useful watermark that anyone could validate, and the author finds the hidden nature unacceptable.
Anthropic itself admits that watermarking cannot reliably be applied to exact material such as programming language code, and would only appear in non-essential parts like comments, where its effect on the actual code is negligible. The author argues that good prose, like code, values exactness in word choice, tone and punctuation, so any scheme that undermines that exactness runs counter to the goal of high-quality generated text.
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