Active Prompting with Chain-of-Thought for Large Language Models

Understand

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.