this post was submitted on 10 Sep 2023
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[–] [email protected] 58 points 1 year ago (2 children)

I know a couple teachers (college level) that have caught several gpt papers over the summer. It’s a great cheating tool but as with all cheating in the past you still have to basically learn the material (at least for narrative papers) to proof gpt properly. It doesn’t get jargon right, it makes things up, it makes no attempt to adhere to reason when it’s making an argument.

Using translation tools is extra obvious—have a native speaker proof your paper if you attempt to use an AI translator on a paper for credit!!

[–] [email protected] 14 points 1 year ago (1 children)

it makes things up, it makes no attempt to adhere to reason when it’s making an argument.

It doesn't hardly understand logic. I'm using it to generate content and it continuously will assert information in ways that don't make sense, relate things that aren't connected, and forget facts that don't flow into the response.

[–] [email protected] 10 points 1 year ago* (last edited 1 year ago) (1 children)

As I understand it as a layman who uses GPT4 quite a lot to generate code and formulas, it doesn't understand logic at all. Afaik, there is currently no rational process which considers whether what it's about to say makes sense and is correct.

It just sort of bullshits it's way to an answer based on whether words seem likely according to its model.

That's why you can point it in the right direction and it will sometimes appear to apply reasoning and correct itself. But you can just as easily point it in the wrong direction and it will do that just as confidently too.

[–] Aceticon 7 points 1 year ago (1 children)

It has no notion of logic at all.

It roughly works by piecing together sentences based on the probability of the various elements (mainly words but also more complex) being there in various relations to each other, the "probability curves" (not quite probability curves but that's a good enough analog) having been derived from the very large language training sets used to train them (hence LLM - Large Language Model).

This is why you might get things like pieces of argumentation which are internally consistent (or merelly familiar segments from actual human posts were people are making an argument) but they're not consistent with each other - the thing is not building an argument following a logic thread, it's just putting together language tokens in common ways which in its training set were found associate with each other and with language token structures similar to those in your question.

[–] CosmicCleric 2 points 1 year ago

That's a great summary of how it works. Well done.