LLM Text Watermarking via Pseudo-Random Sampling for Model-Free Detection
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Solution Overview
Problem
Existing detection methods for machine-generated content are ineffective against malicious use, such as disinformation and spam, due to reliance on model access or degrading text quality, and there is a need for imperceptible watermarking techniques that can be detected without compromising text quality or requiring model access.
Innovation Solution
A method involving pseudo-random scoring of words in the sampling process of large language models (LLMs) to embed watermarks, which can be detected algorithmically without model access, using statistical tests and information-theoretic frameworks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If existing detection methods rely on statistical analysis or access to model internal parameters, then detection capability is improved, but reliability deteriorates when models are proprietary or adversaries use paraphrasing to evade detection
Solution Approach 1:
The patent introduces a watermarking system that acts as an intermediary between the LLM and the detection process. Watermarks are embedded in the generated text through modified sampling probabilities, creating a detectable signal that doesn't require access to model parameters. The detection side uses statistical tests on the watermarked text to identify synthetic content, eliminating the need for proprietary model access while maintaining reliable detection even against paraphrasing attempts
Solution Approach 2:
The patent modifies the sampling parameters of the LLM by introducing watermarking probabilities. During text generation, the model is biased toward selecting tokens that contain watermark signals with a controlled probability. This parameter change in the sampling process embeds detectable patterns into the text without changing the model's underlying parameters, allowing reliable detection without requiring access to the model's internal state
2Measurement precision
If detection methods are applied to machine-generated text, then detection accuracy is improved, but text quality deteriorates
Solution Approach 1:
The patent applies local quality by introducing watermarks at specific positions in the text with controlled probabilities rather than uniformly modifying the entire text. The watermarking process selectively biases token selection at each position based on pre-computed watermark probabilities, ensuring that only a small fraction of tokens contain detectable signals. This localized approach maintains overall text quality while embedding sufficient detection signals for accurate identification of synthetic content
3Difficulty of detecting and measuring
If watermarking is embedded in generated text, then detection capability is improved, but device complexity increases
Solution Approach 1:
The patent segments the watermarking process into independent components: a watermark generation module that creates watermark probability distributions, a text generation module that incorporates watermarks during sampling, and a detection module that analyzes the watermarked text using statistical tests. This segmentation allows each component to be implemented and optimized independently, reducing overall system complexity while maintaining effective detection capability
Solution Approach 2:
The watermarking system is designed to be self-service by using the LLM's own sampling process to embed watermarks. The model generates text while simultaneously incorporating watermark signals through modified probability distributions, eliminating the need for separate post-processing steps. The detection side similarly uses standard statistical analysis on the generated text, avoiding the need for complex model inversion or parameter extraction
Data Source
AI summary
Systems, methods, apparatuses, and computer program products for providing and detecting watermarking in machine generated content. A method may include generating text comprising modifying a sampling process of the LLM by assigning pseudo-random scores to words of the text. The pseudo-random score of a word is related to a sampling likelihood of the word.