About optionally diversifying prompt-selection

[Dicer-Zz]

Thanks for the great idea and the result!

As the title says, I'd like to know how to use optionally diversifying prompt-selection, I don't see where to use the arguments for this method, nor do I see an implementation of it in . /models/prompt.py

I would like to ask about how to normalize the frequency of each prompt into a penalty factor, I don't see a specific description in the paper.

[KingSpencer]

Hi thanks for your question. Actually in this repo, we use a simpler yet as effective version. The optional argument to control the diversified prompt selection is config.use_prompt_mask. If set True, at training time, only disjoint sets of prompts are trained for each task so that we kind of force the model to use different prompts.

[GengDavid]

@KingSpencer
I have the same question as @Dicer-Zz. You mentioned the option "use_prompt_mask" to use "disjoint sets of prompts " for each task.
However, since there are only 10 prompts in the pool, top-k*num_tasks is larger than the number of prompts. I think this option cannot have the same effect (i.e., "diversify" the selection) as the paper said.