DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI

Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong

DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI: 13 upvotes on Hugging Face Daily Papers, #6 of 10 papers on 2023-07-20. Day-by-day upvote history.

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 page on Hugging Face · arXiv

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