Show HN: Training a model to identify AI web content from structure alone
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Show HN: Training a model to identify AI web content from structure alone
Unofficial Hacker News client; not affiliated with Y Combinator.
bryanrasmussen · · focus · HN ↗
Idea 1: Identify the worst most boring human marketing, organizational, bureaucratic texts from the a time before AI was writing it, anonymize this text to make sure there is no reference to current events that can be used to determine that it is not AI. And then see if the AI will say hey, that is not AI slop.
Idea 2: Have people parody AI slop. Can it determine the parody is still not AI slop?
bryanrasmussen · · focus · HN ↗
I agree it is somewhat close to the study, but we are not sure, because it is not known how much is really boring dull marketing copy. In choosing blog posts from pre-AI times I suppose you might have difficulty finding the worst examples, and might accidentally get higher quality work.
>Using the Wayback Machine, we collected 2,250 blog posts from 268 B2B company websites that were written before ChatGPT existed
Not sure what metric was used to determine these 2250 blog posts? But there are certainly a lot of ways they can select higher quality posts by accident.
on edit: evidently the Jochen from the study, maybe they thought your comment was AI written.
bryanrasmussen · · focus · HN ↗
You anonymized domains of pre-AI sources, are there any domains that had an excessive number of "telling you the same thing three times" or other AI tells among them?
Of the percentage that was misidentified, do they come from any sources in particular?
If you get a lot of content from these sources and run against the model do they perform worse? If they do how do they perform with word choice detectors? I would expect that structural slop is related to word choice slop among humans.
Anyway these are things I would be interested in as being the point where AI slop rubs up against the human slop which it learned from.