Are Human-generated Demonstrations Necessary for In-context Learning?

• URL: http://arxiv.org/abs/2309.14681v4
• Date: Wed, 21 Feb 2024 05:49:26 GMT
• Title: Are Human-generated Demonstrations Necessary for In-context Learning?
• Authors: Rui Li, Guoyin Wang, Jiwei Li
• Abstract summary: Self-contemplation prompting strategy (SEC) is a paradigm free from human-crafted demonstrations. Extensive experiments in arithmetic reasoning, commonsense reasoning, multi-task language understanding, and code generation benchmarks, show that SEC significantly outperforms the zero-shot learning strategy.
• Score: 22.783456038837794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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