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#watermarking#chatgpt#largelanguagemodels

Ch(e)at GPT? - Computerphile

Computerphile

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  • Recommended by 1 person: 1 on Hacker News.
  • Presented by Mike Pound, University of Nottingham

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.com

Why it's here

1 person recommends this

What people say. Recommended as an approachable and clear explanation of how LLM outputs can be watermarked and detected.

kimburgesson Hacker News • Recommends

On “ShareGPT: Share your ChatGPT conversations with one click”

Watermarking. From an outsiders perspective, the issue appears to reaching consensus on how this can be implemented (but not in the technical sense). There's a game theoretic challenge in that if models define and publish detection mechanisms, this creates a motivation for people to use other systems that don't include this. On the technical front there's a good paper here: link , and a nice very approachable video explaining it here: link to this video .

piersj225on Hacker News • Mentions

On “CheatGPT”

I've heard one idea of adding weights to words. The idea being if I someone hands in a essay with a higher frequency of certain words, there is a very good chance its come from a language model. link to this video I'm not sure this helps with coding though, maybe variable names?

stcgon Hacker News • Mentions

On “Research shows we can only accurately identify AI writers about 50% of the time”

I think watermarking is a better solution. There is a paper on it: Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. (2023). A watermark for large language models. arXiv preprint arXiv:2301.10226. ( link ) Also, Computerphile did a video on it: link to this video But of course, watermarking or your proposed solution (checksums) only work while the general public cannot run a tool like ChatGPT locally on personal computers. And it's only a matter of time before that happens. So in the long run, we need to either: 1. take away control from the users over their personal computers, in the form of what I would call 'AI DRM' (I strongly oppose this option), or 2. legislate: enfor…

rjmunroon Hacker News • Mentions

On “GPT detectors are biased against non-native English writers”

There's an interesting scheme described here: link to this video Essentially by biasing the LLM's token choices slightly you can infer with high probability later that the text was generated by it.

Zambyteon Hacker News • Mentions

On “Meta Launches AI Studio in US”

I wonder if they have a watermark built in that allows them to detect text generated by Llama easily. link to this video link

m-chrzanon Hacker News • Mentions

On “How Claude marks AI-generated content”

There's a computerphile video ( link to this video ) with Dr. Mark Pound explaining a paper by John Kirchenbauer, Jonas Geiping et al. ( link ) that described a method for watermarking LLM output like this. It's not directly stated anywhere in the Claude support article that this is what they're using, but the properties of the watermark described seem to point to this method.