A mismatch between the problem that a system developed to solve and the task that it is given can have significant consequences. Just as the human drive to obtain sweet and fatty foods can be maladaptive in a world where those foods are easily available, the autoregressive tendencies of LLMs can cause problems when they are given a task that is not next-word prediction.
Embers of autoregression show how large language models are shaped by the problem they are trained to solve
I love it that the junk_food / information_diet metaphor can also be applied in reverse, where the entities feeding on the wrong stuff are machines and not humans.
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