Google Releases AI Text Watermarking Tool for Open Source Use

Understanding How LLMs Generate Text

Large Language Models (LLMs), like the ones powering various AI applications today, utilize a unique method to generate coherent and contextually relevant text. They do this one token at a time. Each token can represent a character, a word, or part of a phrase, collectively forming the structure of the generated content.

The Token Prediction Process

When tasked with completing a phrase such as "My favorite tropical fruits are __.", the LLM predicts potential continuations. Some likely candidates could include "mango," "lychee," "papaya," or "durian." Each of these tokens is associated with a probability score, indicating how likely the model considers that particular option to be the next word in the sequence.

Adjusting Probability Scores

In scenarios where a variety of tokens could be appropriate, tools like SynthID enable adjustments to the probability scores assigned to these tokens. This adjustment process happens without compromising the overall quality, accuracy, or creativity of the generated text. It helps in fine-tuning the output to better meet the expectations of users.

Marshalling Complexity

Throughout the text generation process, the predictive cycle is repeated multiple times. A single sentence might utilize ten or more adjusted probability scores, leading to a comprehensive and nuanced output. If you imagine a page filled with text, it could contain hundreds of these probability scores, each contributing to the coherence and flow of the narrative.

Watermarking in Generated Text

The final arrangement of scores, resulting from both the model’s word choices and the applied adjustments, forms what is referred to as the watermark. This watermark is a crucial component, as it can be used to identify content that has been generated by the model and helps maintain a standard of authenticity across the platform.

Conclusion

Understanding how LLMs generate text reveals the complexity and sophistication behind seemingly simple sentences. Each output is the result of an intricate interplay between probability, context, and creative constraints, ensuring that the generated content is as relevant and useful as possible for its intended audience.