Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: IDPG: An Instance-Dependent Prompt Generation Method   Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a
task-specific prompt in each input instance during the model training stage. It
freezes the pre-trained language model and only optimizes a few task-specific
prompts. In this paper, we propose a conditional prompt generation method to
generate prompts for each input instance, referred to as the Instance-Dependent
Prompt Generation (IDPG). Unlike traditional prompt tuning methods that use a
fixed prompt, IDPG introduces a lightweight and trainable component to generate
prompts based on each input sentence. Extensive experiments on ten natural
language understanding (NLU) tasks show that the proposed strategy consistently
outperforms various prompt tuning baselines and is on par with other efficient
transfer learning methods such as Compacter while tuning far fewer model
parameters.

5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
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The categories of this paper are: stat
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat