Genie：在基於內容的資料集生成中實現人類水準

Genie: Achieving Human Parity in Content-Grounded Datasets Generation

January 25, 2024

作者: Asaf Yehudai, Boaz Carmeli, Yosi Mass, Ofir Arviv, Nathaniel Mills, Assaf Toledo, Eyal Shnarch, Leshem Choshen

cs.AI

摘要

對於與內容相關的生成任務而言，缺乏高質量數據已被識別為推動這些任務的一個主要障礙。為了解決這一問題，我們提出了Genie，一種新穎的方法，用於自動生成高質量的與內容相關的數據。它包括三個階段：(a) 內容準備，(b) 生成：從內容中創建任務特定的示例（例如問答對或摘要），(c) 過濾機制，旨在確保生成數據的質量和忠實度。我們通過生成三個大規模的合成數據，即願望，展示了這種方法論的應用範例，用於長文問答（LFQA）、摘要和信息提取。在人類評估中，我們生成的數據被認為自然且高質量。此外，我們將在我們的數據上訓練的模型與在人類編寫的數據上訓練的模型進行比較--對於LFQA是ELI5和ASQA，對於摘要是CNN-DailyMail。我們展示了我們的模型與在人類生成數據上訓練的模型不相上下，甚至在忠實度上始終優於它們。最後，我們應用我們的方法在醫學領域內創建LFQA數據，並將在此數據上訓練的模型與在其他領域上訓練的模型進行比較。

English

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.