  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.
