Generated Knowledge Prompting for Commonsense Reasoning

Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi


Abstract
It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models. To investigate this question, we develop generated knowledge prompting, which consists of generating knowledge from a language model, then providing the knowledge as additional input when answering a question. Our method does not require task-specific supervision for knowledge integration, or access to a structured knowledge base, yet it improves performance of large-scale, state-of-the-art models on four commonsense reasoning tasks, achieving state-of-the-art results on numerical commonsense (NumerSense), general commonsense (CommonsenseQA 2.0), and scientific commonsense (QASC) benchmarks. Generated knowledge prompting highlights large-scale language models as flexible sources of external knowledge for improving commonsense reasoning.Our code is available at github.com/liujch1998/GKP
Anthology ID:
2022.acl-long.225
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3154–3169
Language:
URL:
https://aclanthology.org/2022.acl-long.225
DOI:
10.18653/v1/2022.acl-long.225
Bibkey:
Cite (ACL):
Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, and Hannaneh Hajishirzi. 2022. Generated Knowledge Prompting for Commonsense Reasoning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3154–3169, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Generated Knowledge Prompting for Commonsense Reasoning (Liu et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.225.pdf
Code
 liujch1998/gkp
Data
CommonsenseQAConceptNetNumerSenseQASC