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.
@article{DBLP:journals/corr/abs-2401-14367,
author = {Asaf Yehudai and
Boaz Carmeli and
Yosi Mass and
Ofir Arviv and
Nathaniel Mills and
Assaf Toledo and
Eyal Shnarch and
Leshem Choshen},
title = {Genie: Achieving Human Parity in Content-Grounded Datasets Generation},
journal = {CoRR},
volume = {abs/2401.14367},
year = {2024},
url = {https://doi.org/10.48550/arXiv.2401.14367},
doi = {10.48550/ARXIV.2401.14367},
eprinttype = {arXiv},
eprint = {2401.14367},
timestamp = {Tue, 06 Feb 2024 00:00:00 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2401-14367.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
See the full reference list in the paper.