Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction

Yuhang Wang, Dongyuan Lu, Chao Kong, Jitao Sang

Abstract
Many works employed prompt tuning methods to automatically optimize prompt queries and extract the factual knowledge stored in Pre-trained Language Models. In this paper, we observe that the optimized prompts, including discrete prompts and continuous prompts, exhibit undesirable object bias. To handle this problem, we propose a novel prompt tuning method called MeCoD consisting of three modules: Prompt Encoder, Object Equalization and Biased Object Obstruction. Experimental results show that MeCoD can significantly reduce the object bias and at the same time improve accuracy of factual knowledge extraction.
Anthology ID:
2023.findings-acl.270
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4420–4432
Language:
URL:
https://aclanthology.org/2023.findings-acl.270
DOI:
10.18653/v1/2023.findings-acl.270
Bibkey:
Cite (ACL):
Yuhang Wang, Dongyuan Lu, Chao Kong, and Jitao Sang. 2023. Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction. In Findings of the Association for Computational Linguistics: ACL 2023, pages 4420–4432, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction (Wang et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/naacl24-info/2023.findings-acl.270.pdf
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