Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

• URL: http://arxiv.org/abs/2305.14233v1
• Date: Tue, 23 May 2023 16:49:14 GMT
• Title: Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
• Authors: Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, Bowen Zhou
• Abstract summary: We first provide a systematically designed, diverse, informative, large-scale dataset of instructional conversations, UltraChat. Our objective is to capture the breadth of interactions that a human might have with an AI assistant. We fine-tune a LLaMA model to create a powerful conversational model, UltraLLaMA.
• Score: 91.98516412612739
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}}.

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