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

TeGit: Generating High-Quality Instruction-Tuning Data with Text-Grounded Task Design