Severe data imbalance naturally exists among web-scale vision-language datasets.

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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.

Withincreasingscale,outliersbecome more and more common in all layers of the transformer, and they occur in almost all sequence dimensions.

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These components enhance the model's ability to extract