Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA?

Cunxiang Wang, Pai Liu, Yue Zhang


Abstract
Recent work has investigated the interesting question using pre-trained language models (PLMs) as knowledge bases for answering open questions. However, existing work is limited in using small benchmarks with high test-train overlaps. We construct a new dataset of closed-book QA using SQuAD, and investigate the performance of BART. Experiments show that it is challenging for BART to remember training facts in high precision, and also challenging to answer closed-book questions even if relevant knowledge is retained. Some promising directions are found, including decoupling the knowledge memorizing process and the QA finetune process, forcing the model to recall relevant knowledge when question answering.
Anthology ID:
2021.acl-long.251
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3241–3251
Language:
URL:
https://aclanthology.org/2021.acl-long.251
DOI:
10.18653/v1/2021.acl-long.251
Bibkey:
Cite (ACL):
Cunxiang Wang, Pai Liu, and Yue Zhang. 2021. Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA?. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3241–3251, Online. Association for Computational Linguistics.
Cite (Informal):
Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA? (Wang et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.251.pdf
Optional supplementary material:
 2021.acl-long.251.OptionalSupplementaryMaterial.pdf
Video:
 https://aclanthology.org/2021.acl-long.251.mp4
Code
 wangcunxiang/Can-PLM-Serve-as-KB-for-CBQA
Data
Natural QuestionsSQuADTriviaQAWebQuestions