What are the main contributions of p tuning?

[2catycm]

If it is just an implementation of existing methods, which is not novel, why the conference of p tuning paper is top CCF-A and the paper is widely cited?

So I wonder what is the core difference between p tuning and prefix tuning and deep soft prompt tuning.

From my literatur review, it seems preprending K and V is not proposed in prefix tuning, but many papers wrongly think prefix tuning is changing K V.
So is it actually your inventions? to my knowledge，prefix tuning is like deep visual prompt tuning in jia's paper, which proposed to prepend the x at each layer,not KV.

I found it worth noting that your work is utilizing KV cache that hf transformefs would have as an important implementation predicate. is it also a contribution?

[2catycm]

i have read your paper, but i am not familiar with nlp terms, so i cannot understand your contributions. in the paper,it seems your method is exactly the same with prefix tuning and p tuningv1,just changing the evaluation dataset from nlp to nlu. In your methods section, you made a table to clarify your contribution, saying that your method have Reparam.
Deep PT
Multitask
No verb

But i got confused because it is not directly explained in the paper about what these terms are.
I have the following questions:

• to my best knowledge,soft prompt tuning methods are not “reparameterizable” in the terms of lora paper, but it seems your reparameterizable has a different definition,and what is that based on?
• why p tuningv1 and prefix cannot multitask
• what is differnt not having a verbalizer in nlp