and A(2)

4

5

4

(score ≥ 4) and A(2)

(w/o curation), self-selected data in A(2)
(score ≥ 4.5) categories. Results are
shown in Figure 3. We find that training on augmented data without self-curation does not improve
instruction following performance despite scaling up data quantity. However, training on the high
quality portion of the augmented data leads to increasing instruction following performance, with
steady improvement as we continue to scale up the amount of augmented data. Prior work proposed
the “superficial alignment hypothesis", that only a few thousands of high-quality instruction following
examples are sufficient for aligning a pretrained base model to follow instructions Zhou et al. [2023].
Our results provide a contrasting observation that increasing the quantity of high-quality data provides
further gains (whereas increased quantities of low-quality data does not).

5
Self-AlignmentwithInstructionBacktranslation