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

翻译：尽管对话式人工智能取得了进展，但语言模型在应对多样化对话任务时仍面临挑战，现有对话数据集集合往往缺乏多样性和全面性。为解决这些问题，我们引入了DialogStudio：规模最大且多样性最强的对话数据集集合，所有数据采用统一格式保存并保留原始信息。该集合涵盖开放域对话、任务导向对话、自然语言理解、对话推荐、对话摘要及知识驱动对话等类型，成为对话研究与模型训练异常丰富多样的资源。为提升DialogStudio的实用性，我们明确了每个数据集的许可证，并为选定对话设计了领域感知提示以促进指令感知微调。此外，我们利用该数据集集合开发了对话式AI模型，在零样本和少样本学习场景下的实验均证明了DialogStudio的优越性。为提高透明度，支持基于数据集和任务的研究以及语言模型预训练，所有与DialogStudio相关的数据集、许可证、代码和模型均已在 https://github.com/salesforce/DialogStudio 公开提供。