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2.6M subscribers • 70 videos on Hallway
Intermediate. Assumes working knowledge.
616K views3 years ago1 recommendation
AI and machine learningSecurityTransformers and LLMsCryptography
Mike Pound explains a method for watermarking large language model outputs by biasing token selection to make generated text statistically detectable.
Mike explains a paper from the University of Maryland, proposing a neat trick to 'watermark' the output of large language models such as ChatGPT. Dr Mike Pound is an image analyst at the University of Nottingham. Since making this video, the authors of the paper have been in touch with Mike about a demo of this tech you can try yourself: bit.ly/C_GPT_Watermark_paper The University of Maryland paper: bit.ly/C_LanguageModelWatermarkPaper facebook.com/computerphile twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at bradyharan.comWhat people say. Recommended as an approachable and clear explanation of how LLM outputs can be watermarked and detected.
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