Most public instruction finetuning datasets are relatively small compared to
the closed source datasets used to train industry models. To study questions
about finetuning at scale, such as curricula and learning rate cooldown
schedules, there is a need for industrial-scale datasets. However, this scale
necessitates a data generation process that is almost entirely automated. In
this work, we study methods for generating large instruction datasets from a
single prompt. With little human oversight, we get LLMs to write diverse sets
of instruction examples ranging from simple completion tasks to complex
multi-turn dialogs across a variety of subject areas. When finetuning a Llama-3
8B base model, our dataset meets or exceeds both WizardLM and Ultrachat on both
knowledge-intensive leaderboard tasks as well as conversational evaluations. We
release our dataset, the "generator" prompts that created it, and our finetuned
model checkpoints.

研究如何从单个提示生成大型指导数据集，使用较少的人工监督，使 LLMs 能够撰写多样化的指令示例，从简单的完成任务到复杂的多轮对话，并在知识密集型任务和对话评估中超过了 WizardLM 和 Ultrachat，在扩展性和可伸缩性方面超过了公共指导微调数据集。