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Comment by arduanika | Hacker News Reader

LLMs are, in a sense, compression. Somewhere in that compressed data, this false story is stored. That's clear from the fact that the error is reproducable.

It's not the victim's fault that the data is stored in a manner that isn't convenient for OpenAI to delete. The information is still there, just compressed.

> Relatedly, this article doesn't touch at all on how this false information came to be reported in the first place.

And what was he wearing?

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The "compression" you refer to is lossy. And the "decompression" is different each time you run the model (because of the temperature). You can't "remove" it.

Well then it sounds like they'd better start a new training run, and figure out how to get it right this time. The decompression was similar enough to repro the libel multiple times. And who knows how many other people it's libeling in a similar manner.

> You can't "remove" it

Your honor, my machine here just does crimes, it's how it works. I can't just "remove" the criminality. Something something temperature.

And whose fault is this?

LLMs are not compression. That's an incorrect and misleading model to use here, leading you to a false conclusion.

If you had a loom and yarn, you could load and run it in a certain way to create a blanket with an offensive image. That doesn't mean the loom or yarn has the compressed knowledge of that image.

But if the loom and yarn keep making a specific offensive image or offensive images about a specific topic, because, oh, I don't know, the yarn was previously made in the shape of that image and retains some kinks and variations in tension that make it tend to fall in that particular shape, it would be silly to pretend there's no relationship there.

Is there any evidence that ChatGPT is continuously making this false claim to multiple users? Can the false claim about this specific person be reproduced by anyone else or did it just appear randomly?

I can't reproduce the error. Is there any evidence that others are getting the same answer?

The article explains this, and the point of argument is whether what ChatGPT did to filter out wrong responses is sufficient vs. removing this underlying "internal data".

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