However, text-davinci-002 is able to benefit more substantially. We further show that explanations generated by the LLMs may not entail the models' predictions norbefactually grounded intheinput, evenonsimple tasks with extractive explanations. However, these flawed explanations can still be useful as a way to verify LLMs' predictions post-hoc.

Weprovideargumentsfortreating the learned non-convergent short-run MCMC as a valid model. We show thatthe learned short-run MCMC is capable of generating realistic images.

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