  The increasing scale of large language models (LLMs) brings emergent
abilities to various complex tasks requiring reasoning, such as arithmetic and
commonsense reasoning. It is known that the effective design of task-specific
prompts is critical for LLMs' ability to produce high-quality answers. In
particular, an effective approach for complex question-and-answer tasks is
example-based prompting with chain-of-thought (CoT) reasoning, which
significantly improves the performance of LLMs. However, current CoT methods
rely on a fixed set of human-annotated exemplars, which are not necessarily the
most effective examples for different tasks. This paper proposes a new method,
Active-Prompt, to adapt LLMs to different tasks with task-specific example
prompts (annotated with human-designed CoT reasoning). For this purpose, we
propose a solution to the key problem of determining which questions are the
most important and helpful ones to annotate from a pool of task-specific
queries. By borrowing ideas from the related problem of uncertainty-based
active learning, we introduce several metrics to characterize the uncertainty
so as to select the most uncertain questions for annotation. Experimental
results demonstrate the superiority of our proposed method, achieving
state-of-the-art on eight complex reasoning tasks. Further analyses of
different uncertainty metrics, pool sizes, zero-shot learning, and
accuracy-uncertainty relationship demonstrate the effectiveness of our method.
Our code will be available at https://github.com/shizhediao/active-prompt.
