  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.
