High-quality instruction-tuning data is critical to improving LLM
capabilities. Existing data collection methods are limited by unrealistic
manual labeling costs or by the hallucination of relying solely on LLM
generation. To address the problems, this paper presents a scalable method to
automatically collect high-quality instructional adaptation data by training
language models to automatically design tasks based on human-written texts.
Intuitively, human-written text helps to help the model attenuate illusions
during the generation of tasks. Unlike instruction back-translation-based
methods that directly take the given text as a response, we require the model
to generate the \textit{instruction}, \textit{input}, and \textit{output}
simultaneously to filter the noise. The results of the automated and manual
evaluation experiments demonstrate the quality of our dataset.

通过训练语言模型自动设计任务而收集高质量的教学适应数据的可扩展方法，自动生成任务指导、输入和输出，用于过滤噪音，并通过自动和手工评估实验来证明数据集的质量。