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> kill off the current AI powered solution, because they have no way of explaining That's not correct. Some solution look at perplexity for specific models, so
by viraptor 2y ago
> kill off the current AI powered solution, because they have no way of explaining
That's not correct. Some solution look at perplexity for specific models, some will look at ngram frequencies, and similar approaches. Almost all of those can produce a heatmap of "what looks suspicious". I wouldn't expect any of the detection systems to be like black boxes relying on LLM over the whole text.
- mrweasel 2y agoSorry if this is "moving the goal post", but I wouldn't call looking at ngram frequencies for AI. Producing a heatmap doesn't tell you why something is suspicious, but it's obviously better than telling you nothing. In any case, if you where to use LLMs, or other black box solutions, you'd have to yank those out, if you where met with a requirement to explain why something is suspicious.
- viraptor 2y agoIt's literally the explanation. The only identification we have now is "this local part is often used by an AI model" and "this global structure is often used by an AI model". There's nothing more fancy about it. The heatmap would literally just point out "this part is suspiciously unlikely" - that's the explanation because that's the classification systems use.
- deleted 2y ago[deleted]