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Large Language Models (LLMs) have attained the impressive capability toresolve a wide range of NLP tasks by fine-tuning high-quality instruction data.However, collecting human-written data of high quality, especially multi-turndialogues, is expensive and unattainable for most people. Though previousstudies have used powerful LLMs to generate the dialogues automatically, theyall suffer from generating untruthful dialogues because of the modelhallucination. Therefore, we propose a method called RefGPT to generateenormous truthful and customized dialogues without worrying about factualerrors caused by the model hallucination. RefGPT solves the model hallucinationin dialogue generation by restricting the LLMs to leverage the given referenceinstead of reciting their own knowledge to generate dialogues. Additionally,RefGPT adds detailed controls on every utterance to enable high customizationcapability, which previous studies have ignored. On the basis of RefGPT, wealso propose two high-quality dialogue datasets generated by GPT-4, namelyRefGPT-Fact and RefGPT-Code. RefGPT-Fact is a dataset with 100k multi-turndialogues based on factual knowledge and RefGPT-Code has 76k multi-turndialogues covering a wide range of coding scenarios. Our code and datasets arereleased in https://github.com/mutonix/RefGPT.

RefGPT: Dialogue Generation of GPT, by GPT, and for GPT