Instruction Pre-Training: Language Models are Supervised Multitask Learners
Daixuan Cheng, Yuxian Gu, Shaohan Huang, Junyu Bi, Minlie Huang, Furu Wei
Abstract
Unsupervised multitask pre-training has been the critical method behind the recent success of language models (LMs).However, supervised multitask learning still holds significant promise, as scaling it in the post-training stage trends towards better generalization.In this paper, we explore supervised multitask pretraining by proposing Instruction Pre-Training, a framework that scalably augments massive raw corpora with instruction-response pairs to pre-train LMs.The instruction-response pairs are generated by an efficient instruction synthesizer built on open-source models.In our experiments, we synthesize 200M instruction-response pairs covering 40+ task categories to verify the effectiveness of Instruction Pre-Training.In pre-training from scratch, Instruction Pre-Training not only consistently enhances pre-trained base models but also benefits more from further instruction tuning.In continual pre-training, Instruction Pre-Training enables Llama3-8B to be comparable to or even outperform Llama3-70B.Our model, code, and data are available at https://github.com/microsoft/LMOps.