Unverified50% confidenceFactExact time
When a large language model generates text, each word is sampled from a probability distribution, and there are many equivalent alternatives between high-probability and low-probability words, providing space for watermark injection.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
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Related Claims
VerifiedThe output of large language models is fundamentally probabilistic text generation, and a single rephrasing of a prompt can change field names, nesting levels, or enumeration values in the output without triggering compile-time or static analysis warnings.76% similarUnverifiedThe bag-of-words model breaks text down into a collection of words, ignoring word order and only counting the frequency or weight of each word's occurrence.75% similarUnverifiedLarge language models encode textual statistical patterns into parameters via backpropagation and do not store raw data in the traditional sense, but high-frequency text may produce memorization phenomena and be reproduced nearly verbatim.74% similarVerifiedText watermarking is a proactive scheme that makes slight, rule-based adjustments to the probability distribution of a language model during the generation of each token, causing the output text to carry a signature that can be recovered algorithmically.74% similarUnverifiedLLM-generated text tends to select high-probability words and common collocations, causing the output to exhibit relatively low entropy in its vocabulary distribution.73% similar
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