title: DialogStudio: Towards Richest and Most Diverse Unified Dataset
    Collection for Conversational AI
summary: Despite advancements in conversational AI, language models encounter
  challenges to handle diverse conversational tasks, and existing dialogue
  dataset collections often lack diversity and comprehensiveness. To tackle these
  issues, we introduce DialogStudio: the largest and most diverse collection of
  dialogue datasets, unified under a consistent format while preserving their
  original information. Our collection encompasses data from open-domain
  dialogues, task-oriented dialogues, natural language understanding,
  conversational recommendation, dialogue summarization, and knowledge-grounded
  dialogues, making it an incredibly rich and diverse resource for dialogue
  research and model training. To further enhance the utility of DialogStudio, we
  identify the licenses for each dataset and design domain-aware prompts for
  selected dialogues to facilitate instruction-aware fine-tuning. Furthermore, we
  develop conversational AI models using the dataset collection, and our
  experiments in both zero-shot and few-shot learning scenarios demonstrate the
  superiority of DialogStudio. To improve transparency and support dataset and
  task-based research, as well as language model pre-training, all datasets,
  licenses, codes, and models associated with DialogStudio are made publicly
  accessible at https://github.com/salesforce/DialogStudio
paper_authors._id: 64b89f79d6ced0fd74b332b5
paper_authors.name: Jianguo Zhang
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paper_authors.fullname: Jianguo Zhang
paper_authors.type: user
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paper_authors.name: Kun Qian
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paper_authors.fullname: Kun Qian
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paper_authors.user: qbetterk
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paper_authors.name: Zhiwei Liu
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paper_authors.fullname: Zhiwei Liu
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paper_authors.user: jimzhiwei
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paper_authors.name: Shelby Heinecke
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paper_authors.name: Rui Meng
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paper_authors.fullname: Rui
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paper_authors.user: memray
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paper_authors.name: Ye Liu
paper_authors._id: 64b89f79d6ced0fd74b332bb
paper_authors.name: Zhou Yu
paper_authors._id: 64be465137b81dbb07682202
paper_authors.name: Huan Wang
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paper_authors.fullname: Huan Wang
paper_authors.type: user
paper_authors.user: joyousprince
paper_authors._id: 64b89f79d6ced0fd74b332bc
paper_authors.name: Silvio Savarese
paper_authors._id: 64b89f79d6ced0fd74b332bd
paper_authors.name: Caiming Xiong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-20T07:42:13.024Z
paper_authors._id: 649dbcc4e0fff1ed099dc80a
paper_authors.avatarUrl: /avatars/c87c273ca628dbcddccbf1ee19b2ce33.svg
paper_authors.fullname: Caiming Xiong
paper_authors.type: user
paper_authors.user: cxiong
paper_title: DialogStudio: Towards Richest and Most Diverse Unified Dataset
    Collection for Conversational AI
paper_summary: Despite advancements in conversational AI, language models encounter
  challenges to handle diverse conversational tasks, and existing dialogue
  dataset collections often lack diversity and comprehensiveness. To tackle these
  issues, we introduce DialogStudio: the largest and most diverse collection of
  dialogue datasets, unified under a consistent format while preserving their
  original information. Our collection encompasses data from open-domain
  dialogues, task-oriented dialogues, natural language understanding,
  conversational recommendation, dialogue summarization, and knowledge-grounded
  dialogues, making it an incredibly rich and diverse resource for dialogue
  research and model training. To further enhance the utility of DialogStudio, we
  identify the licenses for each dataset and design domain-aware prompts for
  selected dialogues to facilitate instruction-aware fine-tuning. Furthermore, we
  develop conversational AI models using the dataset collection, and our
  experiments in both zero-shot and few-shot learning scenarios demonstrate the
  superiority of DialogStudio. To improve transparency and support dataset and
  task-based research, as well as language model pre-training, all datasets,
  licenses, codes, and models associated with DialogStudio are made publicly
  accessible at https://github.com/salesforce/DialogStudio
paper_ai_summary: DialogStudio is a comprehensive and diverse collection of dialogue datasets designed to address challenges in conversational AI and improve dialogue research and model training.
paper_ai_keywords: conversational AI dialogue datasets open-domain dialogues task-oriented dialogues natural language understanding conversational recommendation dialogue summarization knowledge-grounded dialogues instruction-aware fine-tuning zero-shot learning few-shot learning