Re:You can watermark GPT's outputs discreetly
by chaxor on Friday June 02, 2023 @12:09AM
(#36160933)
The only real way to block specifically openAI generated content (or some other online generated LLM content) is for the company itself to store all of its outputs and compare to that database, like shingling / LSH for plagiarism detection. Other LLMs (local) are completely impossible to block, as it's a constant chase. Any system that tries to estimate the distribution of e.g. a specific LLM that performs beam search with certain parameters, can be easily adjusted to use typical decoding or something else, so it simply will never be possible, hence useless to try to stop.
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It doesn't need to stop the determined users, thete's value in just filtering out articles that are not even proofread by humans.
It's going to be a constant cat and mouse game, I agree, but for now it just need to catch the dead mice.
The phrase "As an AI language model" could be useful here.
Is accusing people of being an AI model going to become the new Godwin's law?
Re:You can watermark GPT's outputs discreetly
by gpderetta on Friday June 02, 2023 @02:55AM
(#36161937)
Accusing people of being chat bots has a long tradition on the internet.
Re:You can watermark GPT's outputs discreetly
by JohnFen on Friday June 02, 2023 @10:52AM
(#36165914)
Yes, but before, it was pretty easy to tell if the accusation is supportable or not. With LLMs, it won't be, so the accusation carries more weight.
Re:You can watermark GPT's outputs discreetly
by ademarre on Friday June 02, 2023 @01:35AM
(#36161419)
Shazam-like fingerprinting for text. The complete LLM outputs wouldn't need to be stored, just the fingerprints along with some mechanism for trusted timestamping (could be Blockchain).
Re:You can watermark GPT's outputs discreetly
by chaxor on Friday June 02, 2023 @09:52AM
(#36165074)
This has been done for a very long time. Blockchains are definitely not required (this isn't just the usual hate from HN of Blockchain, it just actually doesn't make sense here).
Fingerprinting by shingling (windows of text) with some normalization steps is pretty typical in plagiarism or similarity detection. A big database of docid-shingleid pairs along with weights for their frequency is often a very simple and fast way to do this analysis.
The big part is getting OpenAI/anthropic/etc to do it on their data and provide a service for that, but there's obviously a lot of unwanted consequences - specifically storing of all user data (even if the shingled and docids are hashes, it's still info).