  Large language models (LLMs) have shown increasing power on various natural
language processing (NLP) tasks. However, tuning these models for downstream
tasks usually needs exorbitant costs or is unavailable due to commercial
considerations. Recently, black-box tuning has been proposed to address this
problem by optimizing task-specific prompts without accessing the gradients and
hidden representations. However, most existing works have yet fully exploited
the potential of gradient-free optimization under the scenario of few-shot
learning. In this paper, we describe BBT-RGB, a suite of straightforward and
complementary techniques for enhancing the efficiency and performance of
black-box optimization. Specifically, our method includes three plug-and-play
components: (1) Two-stage derivative-free optimization strategy that
facilitates fast convergence and mitigates overfitting; (2) Automatic
verbalizer construction with its novel usage under few-shot settings; (3)
Better prompt initialization policy based on instruction search and
auto-selected demonstration. Extensive experiments across various tasks on
natural language understanding and inference demonstrate the effectiveness of
our method. Our codes are publicly available at
https://github.com/QiushiSun/BBT-RGB.
