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alexcdot

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Problem is that most ML papers today are not independently verifiable proofs - in most, you have to trust the scientist didn't fraudulently produce their results.

There is so much BS being submitted to conferences and decreasing the amount of BS they see would result in less skimpy reviews and also less apathy

Absolutely, expectations and tools given by management are a real problem.

If management fires you because they are wrong about how good AI is, and you're right - at the end of the day, you're fired and the manager is in lalaland.

People need to actually push the correct calibration of what these tools should be trusted to do, while also trying to work with what they have.

really good point. one of the cofounders of gptzero here!

the tool gptzero used in the article also detects if the citation supports the claim too, if you scroll to "cited information accuracy" here: https://app.gptzero.me/documents/1641652a-c598-453f-9c94-e0b...

this is still in beta because its a much harder problem for sure, since its hard to determine if a 40 page paper supports a claims (if the paper claims X is computationally intractable, does that mean algorithms to compute approximate X are slow?)

hey, i'm a part of the gptzero team that built automated tooling, to get the results in that article!

totally agree with your thinking here, we can't just give this to an LLM, because of the need to have industry-specific standards for what is a hallucination / match, and how to do the search