T5 or T5. Additionally, we perform this compar-
ison in the zero-shot setting, thus allowing us to
compare the instruction following capabilities of
these models in the absence of direct in-context
learning. We present this comparison in Figure 8.
This comparison allows us to answer the follow-
ing questions: a) Does increased scale significantly
impact the tasks on which models can perform
above the random baseline, and b) Does enhanced
instruction tuning, including the incorporation of
program code as seen in text-davinci-003, provide
an advantage in being able to perform above the
baseline on tasks? By limiting ourselves to the
zero-shot setting, we ensure that our results are not
affected by in-context capabilities, which we know
to increase significantly with scale. Our results in-
dicate that neither a) nor b) is the case. There is a
significant overlap in the tasks on which Flan-T5
performs above the baseline and those on which
text-davinci-003 does. Not only is there an overlap
AreEmergentAbilitiesinLarge Language Models just In-Context