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.

我们提出了一种可扩展的方法，通过自动标记人工编写的文本与相应的指令来构建高质量的指令跟随语言模型。我们的方法命名为指令反向翻译，使用少量种子数据和给定的网络语料库对语言模型进行微调，通过为网络文档生成指令提示来构建训练样本（自助增强），然后从这些候选样本中选择高质量的例子（自我策划）。然后使用这些数据对模型进行微调。对LLaMa进行两次迭代的微调可以得到一个模型，它在Alpaca排行榜上性能优于其他基于LLaMa的模型，并且不依赖蒸馏数据，展示了高度有效的自我对齐。

指导反向翻译的自对齐