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Comment by themgt

4 hours ago

can and do develop biases based on text

"develop biases" is anthropomorphism. It's like saying "Fable there are two programming languages, mimblewort and bafflewick, which do you choose?"

The results show 51% mimblewort / 49% bafflewick. Fable based it on nothing! I've demonstrated Fable has bias and is unsuited for use in software engineering.

> The results show 51% mimblewort / 49% bafflewick. Fable based it on nothing! I've demonstrated Fable has bias and is unsuited for use in software engineering.

Actually... if that happened (with a delta outside the margin for error/randomness), you did demonstrate a bias!

That's the point - those two made-up things should have resulted in an equal split. If it didn't, then Fable is using something in its training data to lean towards one of them (once again, note that the scientist conducting the trial would have set a P-value before starting).

  • Right, the point is you demonstrated a bias in the scenario of "Fable there are two programming languages, mimblewort and bafflewick, which do you choose?"

    You said in another comment "Difficult to do when you're following a scientific process" - the point is, the scientific process doesn't inherently generalize in the way many are claiming/implying. The scientific process proved an entirely contrived, fake scenario generates stratified output. That's it.

    It's both almost certainly true that Fable 5.1 mimblewort vs. bafflewick would show stratification, and that has ~no relevance on whether Fable is useful for software engineering work.

    That's the point - those two made-up things should have resulted in an equal split.

    That's just your claim about how LLMs "should" work, based on ... your subjective preference?

While I fully agree, we shouldn't anthropomorphize the models, it's also silly to pretend that "develop biases" is understood as implying anthropomorphic features of the thing being discussed. Organizations and abstract bodies develop biases, even datasets are often said to have "developed biases".