Genie: Achieving Human Parity in Content-Grounded Datasets Generation
Asaf Yehudai Boaz Carmeli Yosi Mass Ofir Arviv Nathaniel Mills Assaf Toledo Eyal Shnarch Leshem Choshen
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.