  Prompt-based pre-trained language models (PLMs) paradigm have succeeded
substantially in few-shot natural language processing (NLP) tasks. However,
prior discrete prompt optimization methods require expert knowledge to design
the base prompt set and identify high-quality prompts, which is costly,
inefficient, and subjective. Meanwhile, existing continuous prompt optimization
methods improve the performance by learning the ideal prompts through the
gradient information of PLMs, whose high computational cost, and low
readability and generalizability are often concerning. To address the research
gap, we propose a Dialogue-comprised Policy-gradient-based Discrete Prompt
Optimization ($DP_2O$) method. We first design a multi-round dialogue alignment
strategy for readability prompt set generation based on GPT-4. Furthermore, we
propose an efficient prompt screening metric to identify high-quality prompts
with linear complexity. Finally, we construct a reinforcement learning (RL)
framework based on policy gradients to match the prompts to inputs optimally.
By training a policy network with only 0.67% of the PLM parameter size on the
tasks in the few-shot setting, $DP_2O$ outperforms the state-of-the-art (SOTA)
method by 1.52% in accuracy on average on four open-source datasets. Moreover,
subsequent experiments also demonstrate that $DP_2O$ has good universality,
robustness, and generalization ability.
