task or not.3 We prompt vanilla GPT3 few-shot to
determine this, using 12 classiﬁcation instructions
and 19 non-classiﬁcation instructions from the seed
tasks. The prompting template is shown in Table 7.
Instance Generation. Giventheinstructionsand
their task type, we generate instances for each in-
struction independently. This is challenging be-
cause it requires the model to understand what the
target task is, based on the instruction, ﬁgure out
what additional input ﬁelds are needed and gen-
erate them, and ﬁnally complete the task by pro-
ducing the output. We found that pretrained lan-
guagemodelscanachievethistoalargeextentwhen
prompted with instruction-input-output in-context
examples from other tasks. A natural way to do this
is the Input-ﬁrst Approach, where we can ask a
language model to come up with the input ﬁelds
ﬁrst based on the instruction, and then produce the
corresponding output. This generation order is sim-
ilartohowmodelsareusedtorespondtoinstruction
SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions