and A(2)

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Generated Instructions. We conduct the task diversity analysis of the seed data and augmented
data using the approach from Wang et al. [2022a]. Figure 2 visualizes the distribution of the verb-noun
structure of instructions in the seed data and augmented data (A(2)
category) respectively. Similar to
the seed data, there are a few head tasks related to writing, information seeking and advice, although
the type of content from unlabeled data (article, recipe, description, release, etc.) complements those
in the seed data (essay, script, code, story, etc.). Furthermore, the augmented data increases the task
diversity especially in the long tail.

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3.3 Scaling Analysis

Data quality vs. data quantity.
In order to understand the importance of data quality vs. data
quantity in learning to follow instructions, we compared finetuning on augmented data of different
quality. Specifically, we compared finetuning on augmented data without quality-based selection

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Self-AlignmentwithInstructionBacktranslation