For sequence-to-sequence or generative models,
Kim and Rush (2016) introduced the concept of
sequence-level distillation. This approach involves
generating a synthetic output by running inference
with the teacher model, which is then used to train
the student model. This method is more efﬁcient
as it only requires running the usually large teacher

<example>What are some things you can do to de-stress?</example>
<example>How can individuals and organizations reduce unconscious bias?</example>
<example>Write a program to compute the sum of integers from k to n.</example>

Generate 20 diverse examples that are similar to the provided examples.
You do not need to provide a response to the generated examples.
Each example must include an instruction.
Each generated instruction can be either an imperative sentence or a question.
Each example must start with the label "<example>" and end with the label "</example>".
LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions