The lack of high-quality data for content-grounded generation tasks has been identified as a major obstacle to advancing these tasks. To address this gap, we propose Genie, a novel method for automatically generating high-quality content-grounded data. It consists of three stages: (a) Content Preparation, (b) Generation: creating task-specific examples from the content (e.g., question-answer pairs or summaries). (c) Filtering mechanism aiming to ensure the quality and faithfulness of the generated data. We showcase this methodology by generating three large-scale synthetic data, making wishes, for Long-Form Question-Answering (LFQA), summarization, and information extraction. In a human evaluation, our generated data was found to be natural and of high quality. Furthermore, we compare models trained on our data with models trained on human-written data -- ELI5 and ASQA for LFQA and CNN-DailyMail for Summarization. We show that our models are on par with or outperforming models trained on human-generated data and consistently outperforming them in faithfulness. Finally, we applied our method to create LFQA data within the medical domain and compared a model trained on it with models trained on other domains.

翻译：内容基础生成任务所需的高质量数据匮乏，已被视为制约这类任务发展的主要障碍。为弥补这一不足，我们提出Genie——一种自动生成高质量内容基础数据的新方法。该方法包含三个阶段：(a) 内容准备，(b) 生成：基于内容创建任务特定示例（如问答对或摘要），(c) 过滤机制：旨在确保生成数据的质量和忠实性。我们通过生成三个大规模合成数据集（用于长文本问答、摘要和信息提取）来展示该方法的效能。人工评估表明，我们生成的数据自然且质量高。此外，我们将基于我们数据训练的模型与基于人工撰写数据（长文本问答的ELI5和ASQA，以及摘要的CNN-DailyMail）训练的模型进行对比，结果显示我们的模型与后者性能相当甚至更优，且在忠实性方面始终领先。最后，我们将该方法应用于医学领域的长文本问答数据创建，并将基于该数据训练的模型与基于其他领域数据训练的模型进行比较。