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Watermarking of Large Language Models

Simons Institute for the Theory of Computing

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  • Presented by Scott Aaronson, UT Austin & OpenAI

Advanced. Deep dive for people who know the field.

17K views3 years ago1 recommendation

AI and machine learningSecurityTransformers and LLMsAI safety and impactCryptography

Scott Aaronson discusses a scheme for inserting statistical watermarks into the outputs of large language models to address the AI attribution problem.

Scott Aaronson (UT Austin & OpenAI) simons.berkeley.edu/talks/scott-aaronson-ut-austin-openai-20… Large Language Models and Transformers I'll discuss a scheme for inserting a statistical watermark into the outputs of LLMs, which I developed while working at OpenAI. I'll place this in the context of other theoretical and empirical work on LLM watermarking over the past year, as well as other approaches to the AI attribution problem. I'll also say something about the challenges of deployment, and the unsolved technical problem of designing a text watermarking method that resists translation, paraphrasing, and similar attacks.

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What people say. Recommended as a clear explanation of how statistical signatures can be embedded in LLM outputs to identify AI-generated text.

u/dualmindbladein r/compsci • Recommends

On “AI watermarking makes a lot more sense for text than it does for code”

…is leaves a very obvious statistical signature even in relatively short strings and even if you don't know the entire context window, and that allows you to peg text with extremely high accuracy and to rapidly improve the accuracy to near 100% with a small number of samples. I think this was first pointed out by computer scientist Scott Aaronson: link to this video And here Anthropic confirm they are doing something similar: https://www.anthropic.com/news/claude-text-watermark…
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