6 Conclusion

We proposed a scalable approach to finetune large language models to follow instructions. Our
method leverages large amounts of unlabeled data by developing an iterative self-training algorithm
that we dub instruction backtranslation. Our method uses the model itself to both augment and curate
high quality training examples to improve its own performance. On the Alpaca leaderboard, our
finetuned models outperform all other non-distilled instruction-following models, while using fewer
human annotated examples. Future work should scale this method further by considering larger
unlabeled corpora, which our analysis suggests should yield further gains.

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Self-AlignmentwithInstructionBacktranslation