Calibrating Factual Knowledge in Pretrained Language Models

Qingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, Lei Li


Abstract
Previous literature has proved that Pretrained Language Models (PLMs) can store factual knowledge. However, we find that facts stored in the PLMs are not always correct. It motivates us to explore a fundamental question: How do we calibrate factual knowledge in PLMs without re-training from scratch? In this work, we propose a simple and lightweight method CaliNet to achieve this goal. To be specific, we first detect whether PLMs can learn the right facts via a contrastive score between right and fake facts. If not, we then use a lightweight method to add and adapt new parameters to specific factual texts. Experiments on the knowledge probing task show the calibration effectiveness and efficiency. In addition, through closed-book question answering, we find that the calibrated PLM possesses knowledge generalization ability after finetuning.Beyond the calibration performance, we further investigate and visualize the knowledge calibration mechanism.
Anthology ID:
2022.findings-emnlp.438
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5937–5947
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.438
DOI:
10.18653/v1/2022.findings-emnlp.438
Bibkey:
Cite (ACL):
Qingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. 2022. Calibrating Factual Knowledge in Pretrained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5937–5947, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Calibrating Factual Knowledge in Pretrained Language Models (Dong et al., Findings 2022)
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PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/2022.findings-emnlp.438.pdf
Software:
 2022.findings-emnlp.438.software.zip