An LLM is a machine-learning neuro network trained through data input/output sets; frequently, the text is unlabeled or uncategorized, and the model is using self supervised or semi-supervised learning methodology. Information is ingested, or content entered, into the LLM, and the output is what that algorithm predicts the next word will be. The input can be proprietary corporate data or, as in the case of ChatGPT, whatever data it’s fed and scraped directly from the internet.
to use the right data requires the use of massive, expensive server farms that act as supercomputers.
Those are controlled by parameters, as in millions, billions, and even trillions of them. if the information an LLM has ingested. is biased, incomplete, or otherwise undesirable, then the response it gives could be equally unreliable, bizarre, or even offensive. When a response goes off the rails, data analysts refer to it as “hallucinations,” because they can be so far off track.
"ChatGPT for biases that are implicit — that is, the gender of the person is not obviously mentioned, but only included as information about their pronouns,” Kapoor said. “That is, if we replace “she” in the sentence with “he," ChatGPT would be three times less likely to make an error.”
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