  Instruction tuning has emerged as a promising approach to enhancing large
language models in following human instructions. It is shown that increasing
the diversity and number of instructions in the training data can consistently
enhance generalization performance, which facilitates a recent endeavor to
collect various instructions and integrate existing instruction tuning datasets
into larger collections. However, different users have their unique ways of
expressing instructions, and there often exist variations across different
datasets in the instruction styles and formats, i.e., format inconsistency. In
this work, we propose a framework named Unified Instruction Tuning (UIT), which
calls OpenAI APIs for automatic format transfer among different instruction
tuning datasets such as PromptSource, FLAN and CrossFit. With the framework, we
(1) demonstrate the necessity of maintaining format consistency in instruction
tuning; (2) improve the generalization performance on unseen instructions on
T5-LM-xl; (3) provide a novel perplexity-based denoising method to reduce the
noise of automatic format transfer to make the UIT framework more practical and
a smaller offline model based on GPT-J that achieves comparable format transfer
capability to OpenAI APIs to reduce costs in practice. Further analysis
regarding variations of targeted formats and other effects is intended.
