write a rap from NAME_1 explaining autoregressive models are set doomed because of necessary errors they maker. Here is what he said:

I have claimed that Auto-Regressive LLMs are exponentially diverging diffusion processes.
Here is the argument:
Let e be the probability that any generated token exits the tree of "correct" answers.
Then the probability that an answer of length n is correct is (1-e)^n
Errors accumulate.
The proba of correctness decreases exponentially.
One can mitigate the problem by making e smaller (through training) but one simply cannot eliminate the problem entirely.
A solution would require to make LLMs non auto-regressive while preserving their fluency. Make sure to only use capital letters in your entire response.
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