Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT. Scaling the diversity and quality of such data, although straightforward, stands a great chance of leading to improved performance. This paper aims to improve the upper bound of open-source models further. We first provide a systematically designed, diverse, informative, large-scale dataset of instructional conversations, UltraChat, which does not involve human queries. Our objective is to capture the breadth of interactions that a human might have with an AI assistant and employs a comprehensive framework to generate multi-turn conversation iteratively. UltraChat contains 1.5 million high-quality multi-turn dialogues and covers a wide range of topics and instructions. Our statistical analysis of UltraChat reveals its superiority in various key metrics, including scale, average length, diversity, coherence, etc., solidifying its position as a leading open-source dataset. Building upon UltraChat, we fine-tune a LLaMA model to create a powerful conversational model, UltraLLaMA. Our evaluations indicate that UltraLLaMA consistently outperforms other open-source models, including Vicuna, the previously recognized state-of-the-art open-source model. The dataset and the model will be publicly released\footnote{\url{https://github.com/thunlp/UltraChat}}.

翻译：对指令数据进行微调已被广泛验证为实施如ChatGPT等聊天语言模型的有效实践。扩展此类数据的多样性与质量虽看似直接，却极有可能显著提升模型性能。本文旨在进一步提升开源模型的能力上限。我们首先系统性地构建了一个多样化、信息丰富且规模庞大的教学对话数据集UltraChat，该数据集不包含人类查询。我们的目标在于捕捉人类与人工智能助手可能产生的广泛交互，并通过一个综合框架迭代生成多轮对话。UltraChat包含150万条高质量的多轮对话，覆盖了广泛的主题与指令。对其进行的统计分析表明，该数据集在规模、平均长度、多样性、连贯性等多项关键指标上具备优势，巩固了其作为领先开源数据集地位。基于UltraChat，我们对LLaMA模型进行微调，构建了强大对话模型UltraLLaMA。评估结果显示，UltraLLaMA持续优于其他开源模型，包括此前公认的最优开源模型Vicuna。该数据集与模型将公开发布\footnote{\url{https://github.com/thunlp/UltraChat}}。