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.

翻译：指令微调已成为增强大语言模型遵循人类指令能力的一种有前景的方法。研究表明，增加训练数据中指令的多样性和数量可以持续提升泛化性能，这推动了近期收集各类指令并将现有指令微调数据集整合为更大规模数据集的努力。然而，不同用户表达指令的方式具有独特性，且不同数据集在指令风格和格式上常存在差异，即格式不一致性。本研究提出名为统一指令微调（UIT）的框架，该框架调用OpenAI API实现PromptSource、FLAN和CrossFit等不同指令微调数据集间的自动格式转换。通过该框架，我们：（1）论证了在指令微调中保持格式一致性的必要性；（2）在T5-LM-xl模型上提升了针对未见指令的泛化性能；（3）提出基于困惑度的新型去噪方法，以降低自动格式转换的噪声，使UIT框架更具实用性；同时基于GPT-J开发了成本更低的离线模型，其格式转换能力与OpenAI API相当。后续将进一步分析目标格式变化及其他影响因素。