Self-Alignment with Instruction Backtranslation
Authors: Xian Li,Ping Yu,Chunting Zhou,Timo Schick,Omer Levy,Luke Zettlemoyer,Jason E Weston,Mike Lewis
Presentation: ICLR 2024,Oral

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.