  Prompt learning's fine-tune performance on text classification tasks has
attracted the NLP community. This paper applies it to resume information
extraction, improving existing methods for this task. We created manual
templates and verbalizers tailored to resume texts and compared the performance
of Masked Language Model (MLM) and Seq2Seq PLMs. Also, we enhanced the
verbalizer design for Knowledgeable Prompt-tuning, contributing to prompt
template design across NLP tasks. We present the Manual Knowledgeable
Verbalizer (MKV), a rule for constructing verbalizers for specific
applications. Our tests show that MKV rules yield more effective, robust
templates and verbalizers than existing methods. Our MKV approach resolved
sample imbalance, surpassing current automatic prompt methods. This study
underscores the value of tailored prompt learning for resume extraction,
stressing the importance of custom-designed templates and verbalizers.
