DialogStudio：面向对话式AI的最丰富和最多样化的统一数据集集合
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI
AI总结：
DialogStudio构建了最大且最多样的统一对话数据集集合，覆盖多种对话任务，并通过许可证识别和指令感知微调，在零样本和少样本实验中展现优越性能。
摘要：
尽管对话式AI取得了进展，语言模型在处理多样化的对话任务时仍面临挑战，现有的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，我们推出了DialogStudio：最大且最多样化的对话数据集集合，在保留原始信息的同时以统一格式进行整合。我们的集合涵盖来自开放域对话、任务导向对话、自然语言理解、对话推荐、对话摘要和基于知识的对话的数据，使其成为对话研究和模型训练的极其丰富和多样的资源。为进一步提升DialogStudio的实用性，我们为每个数据集识别了许可证，为选定的对话设计了外部知识和领域感知提示，以促进指令感知的微调。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中的实验证明了DialogStudio的优越性。为提高透明度并支持基于数据集和任务的研究以及语言模型预训练，与DialogStudio相关的所有数据集、许可证、代码和模型均已公开可访问。
英文摘要：
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, design external knowledge and 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\footnote{\url{https://github.com/salesforce/DialogStudio}}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
