Auto-ICL: In-Context Learning without Human Supervision

Jinghan Yang; Shuming Ma; Furu Wei

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arXiv · 2311.09263

Auto-ICL: In-Context Learning without Human Supervision

Jinghan Yang·Shuming Ma·Furu Wei

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Abstract

With in-context learning ability, the performance of large language models can be significantly boosted when provided with appropriate context. However, existing in-context learning methods mainly rely on human-provided contexts, such as labeled examples and explicit instructions. Writing context by humans is labor-intensive on various tasks and limits the model to tasks manageable by humans. To overcome these limitations, we propose Automatic In-Context Learning framework that enables the model to autonomously generate examples and instructions for problem-solving. With experiments across various models and datasets, results show that model-generated contexts outperform human-annotated contexts, including Few-Shot and Few-Shot-CoT methods, and surpass existing self-generated context methods like Zero-CoT and Auto-CoT.

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Jinghan Yang, Shuming Ma, Furu Wei. 2023-11-15. Auto-ICL: In-Context Learning without Human Supervision. https://arxiv.org/abs/2311.09263

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