Many works employed prompt tuning methods to automatically optimize prompt queries and extract the factual knowledge stored in Pretrained 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.

翻译：许多工作采用提示调优方法自动优化提示查询，并提取预训练语言模型中存储的事实知识。在本文中，我们观察到优化后的提示，包括离散提示和连续提示，表现出不良的对象偏差。为解决这一问题，我们提出了一种名为MeCoD的新型提示调优方法，包含三个模块：提示编码器、对象均衡和偏置对象抑制。实验结果表明，MeCoD能够显著减少对象偏差，同时提高事实知识提取的准确性。