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The recent advancement of large language models (LLMs) has been achievedthrough a combo of instruction tuning and human alignment. However, buildingmanually crafted instruction datasets and performing human alignment become thebottleneck for scaling the development of LLMs. In this paper, we exploit theidea of leveraging AI models in lieu of humans as the teacher to train studentLLMs. Our method is inspired by how human students refine their writing skillsby following the rubrics and learning from the revisions offered by theirtutors. Specifically, we employ a teacher LLM to create a curriculum forinstruction tuning of the student LLM, namely Curriculum Instruction TunING(CITING). It encompasses two main steps: (1) the teacher LLM crafts the rubricsfor evaluating the answers corresponding to various types of questions, and (2)the student LLM learns to follow the rubrics and perform self-correction fromthe revision made by the teacher. We further iteratively carry out it to embodythe procedure of CITING. We compare CITING to a series of state-of-the-artbaselines on four datasets. Our method demonstrates strong improvement in termsof articulate, in-depth, and comprehensive by GPT-4 evaluation. Specifically,it achieves an average winning rate of 79.4% over SFT, 73.4% over RLHF, 78.1%over RRHF, and 76.3% over RAFT, respectively.

CITING: Large Language Models Create Curriculum for Instruction Tuning