# Self-Alignment with Instruction Backtranslation

**Authors:** Xian Li, Ping Yu, Chunting Zhou, Timo Schick, Luke Zettlemoyer, Omer Levy, Jason Weston, Mike Lewis  
**arXiv:** [2308.06259](https://arxiv.org/abs/2308.06259)  
**Date:** 2023-08-11  
**Summary page:** https://www.summarizepaper.com/fr/paper/2308.06259v2/

## Abstract

We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions. Our approach, named instruction backtranslation, starts with a language model finetuned on a small amount of seed data, and a given web corpus. The seed model is used to construct training examples by generating instruction prompts for web documents (self-augmentation), and then selecting high quality examples from among these candidates (self-curation). This data is then used to finetune a stronger model. Finetuning LLaMa on two iterations of our approach yields a model that outperforms all other LLaMa-based models on the Alpaca leaderboard not relying on distillation data, demonstrating highly effective self-alignment.