Today, we’re open-sourcing our SynthID text watermarking tool through an updated Responsible Generative AI Toolkit. Available freely to developers and businesses, it will help them identify their AI-generated content. 🔍 Find out more →

Here’s how SynthID watermarks AI-generated content across modalities. ↓

By open-sourcing the code, more people will be able to use the tool to watermark and determine whether text outputs have come from their own LLMs - making it easier to build AI responsibly. We explain more about this tech in @Nature. ↓

The study focuses on developing a method called SynthID-Text to watermark text generated by large language models (LLMs). Watermarking can help identify synthetic text and limit accidental or deliberate misuse of LLMs. The researchers evaluate SynthID-Text across multiple LLMs and find that it provides improved detectability over comparable methods, while maintaining standard benchmarks and human side-by-side ratings that indicate no change in LLM capabilities. They also conduct a live experiment with the Gemini production system, which shows that the difference in response quality and utility, as judged by humans, is negligible between watermarked and unwatermarked responses. full paper:

1. Can we break down the image generation by down-sampling and up-sampling? 2. Invisible to the human eye, but if we plug them back into another gen-AI, would it remove the watermark? For example adding noise to the image, then feeding it back into another watermark-free diffusion model? Asking another LLM to make random modification to a given text? 3. Without regulatory enforcement of these watermarks, I suspect most models won't have them.

Detecting AI-written text is tough without watermarks. Open-sourcing SynthID-Text enables others to embed watermarks in their model outputs. This means there will be two types of models: Models which watermark their outputs and the ones that won't. 🤔

awesome!!! was just looking into this yesterday hoping it was open source :)

Awesome! Really appreciate it.

Oh ok so you're actively polluting the output of the software I am paying for. Sounds like I won't be paying for it anymore.

How does SynthID text’s generative watermarking handle variability across different content domains, and what measures are taken to ensure the watermark’s detectability remains consistent when faced with novel or out-of-distribution input contexts?

very suspicious to announce opensourcing something without saying what license or where to download it