@inproceedings{wang-etal-2023-towards-alleviating,
title = "Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction",
author = "Wang, Yuhang and
Lu, Dongyuan and
Kong, Chao and
Sang, Jitao",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.270",
doi = "10.18653/v1/2023.findings-acl.270",
pages = "4420--4432",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction
%A Wang, Yuhang
%A Lu, Dongyuan
%A Kong, Chao
%A Sang, Jitao
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wang-etal-2023-towards-alleviating
%X 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.
%R 10.18653/v1/2023.findings-acl.270
%U https://aclanthology.org/2023.findings-acl.270
%U https://doi.org/10.18653/v1/2023.findings-acl.270
%P 4420-4432
Markdown (Informal)
[Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction](https://aclanthology.org/2023.findings-acl.270) (Wang et al., Findings 2023)
ACL