Enhancing Chat Language Models by Scaling High-quality Instructional
  Conversations
Ning Ding Yulin Chen Bokai Xu Yujia Qin Zhi Zheng Shengding Hu Zhiyuan Liu Maosong Sun Bowen Zhou
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}}.