@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://preview.aclanthology.org/fix-sig-urls/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."
}