Despite the promising few-shot ability of large language models (LLMs), the standard paradigm of In-context Learning (ICL) suffers the disadvantages of susceptibility to selected demonstrations and the intricacy to generate these demonstrations. In this paper, we raise the fundamental question that whether human-generated demonstrations are necessary for ICL. To answer this question, we propose self-contemplation prompting strategy (SEC), a paradigm free from human-crafted demonstrations. The key point of SEC is that, instead of using hand-crafted examples as demonstrations in ICL, SEC asks LLMs to first create demonstrations on their own, based on which the final output is generated. SEC is a flexible framework and can be adapted to both the vanilla ICL and the chain-of-thought (CoT), but with greater ease: as the manual-generation process of both examples and rationale can be saved. Extensive experiments in arithmetic reasoning, commonsense reasoning, multi-task language understanding, and code generation benchmarks, show that SEC, which does not require hand-crafted demonstrations, significantly outperforms the zero-shot learning strategy, and achieves comparable results to ICL with hand-crafted demonstrations. This demonstrates that, for many tasks, contemporary LLMs possess a sufficient level of competence to exclusively depend on their own capacity for decision making, removing the need for external training data. Code is available at https://github.com/ruili33/SEC.

翻译：尽管大型语言模型（LLMs）展现出有前景的小样本学习能力，但上下文学习（ICL）的标准范式存在对选定示范敏感、且生成这些示范过程复杂的缺陷。本文提出一个根本性问题：人类生成的示范对ICL是否必要？为回答该问题，我们提出自我反思提示策略（SEC）——一种无需人工构造示范的范式。SEC的核心在于：不同于ICL中采用人工设计的示例作为示范，SEC要求LLMs首先自主创建示范，再基于这些示范生成最终输出。SEC是一个灵活框架，可适配于原始ICL和思维链（CoT），且更为简便——因为可省去示例及其推理过程的人工生成步骤。在算术推理、常识推理、多任务语言理解和代码生成基准测试中的大量实验表明：无需人工示范的SEC显著优于零样本学习策略，并与采用人工示范的ICL取得相当结果。这证明在诸多任务中，当代LLMs已具备足够能力完全依赖自身决策能力，无需外部训练数据。代码见https://github.com/ruili33/SEC。