  Prompt tuning in natural language processing (NLP) has become an increasingly
popular method for adapting large language models to specific tasks. However,
the transferability of these prompts, especially continuous prompts, between
different models remains a challenge. In this work, we propose a zero-shot
continuous prompt transfer method, where source prompts are encoded into
relative space and the corresponding target prompts are searched for
transferring to target models. Experimental results confirm the effectiveness
of our method, showing that 'task semantics' in continuous prompts can be
generalized across various language models. Moreover, we find that combining
'task semantics' from multiple source models can further enhance the
generalizability of transfer.
