title: Enhancing Chat Language Models by Scaling High-quality Instructional
    Conversations
summary: 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\url{https://github.com/thunlp/UltraChat}.
paper_authors._id: 646d6610eb9268aeebc436d3
paper_authors.name: Ning Ding
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-24T07:38:25.237Z
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paper_authors.avatarUrl: /avatars/12bcd18d215abf91f297f93007733148.svg
paper_authors.fullname: Ning Ding
paper_authors.type: user
paper_authors.user: stingning
paper_authors._id: 646d6610eb9268aeebc436d4
paper_authors.name: Yulin Chen
paper_authors._id: 646d6610eb9268aeebc436d5
paper_authors.name: Bokai Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-15T16:37:21.240Z
paper_authors._id: 6415818a986557e8cac252bf
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paper_authors.fullname: Bokai Xu
paper_authors.type: user
paper_authors.user: bokesyo
paper_authors._id: 646d6610eb9268aeebc436d6
paper_authors.name: Yujia Qin
paper_authors._id: 646d6610eb9268aeebc436d7
paper_authors.name: Zhi Zheng
paper_authors._id: 646d6610eb9268aeebc436d8
paper_authors.name: Shengding Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-24T07:43:48.835Z
paper_authors._id: 6041bec6c15e823a685f2afc
paper_authors.avatarUrl: /avatars/7d09fbe771d3c3312ac7eb6ef83ce053.svg
paper_authors.fullname: Shengding Hu
paper_authors.type: user
paper_authors.user: ShengdingHu
paper_authors._id: 646d6610eb9268aeebc436d9
paper_authors.name: Zhiyuan Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T08:03:48.916Z
paper_authors._id: 6472f1f7485a7c8e1bcf507b
paper_authors.avatarUrl: /avatars/79dca137583875191ea883b193b630b7.svg
paper_authors.fullname: Zhiyuan Liu
paper_authors.type: user
paper_authors.user: zibuyu9
paper_authors._id: 646d6610eb9268aeebc436da
paper_authors.name: Maosong Sun
paper_authors._id: 646d6610eb9268aeebc436db
paper_authors.name: Bowen Zhou
paper_title: Enhancing Chat Language Models by Scaling High-quality Instructional
    Conversations
paper_summary: 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\url{https://github.com/thunlp/UltraChat}.
paper_ai_summary: A large-scale dataset, UltraChat, and a fine-tuned model, UltraLLaMA, are created to improve open-source chat language models' performance.
paper_ai_keywords: Llama model Vicuna conversational model large-scale dataset instructional conversations multi-turn dialogues coherence diversity fine-tuning