In the era of Large Language Models (LLMs), human-computer interaction has
evolved towards natural language, offering unprecedented flexibility. Despite
this, LLMs are heavily reliant on well-structured prompts to function
efficiently within the realm of In-Context Learning. Vanilla In-Context
Learning relies on human-provided contexts, such as labeled examples, explicit
instructions, or other guiding mechanisms that shape the model's outputs. To
address this challenge, our study presents a universal framework named
Automatic In-Context Learning. Upon receiving a user's request, we ask the
model to independently generate examples, including labels, instructions, or
reasoning pathways. The model then leverages this self-produced context to
tackle the given problem. Our approach is universally adaptable and can be
implemented in any setting where vanilla In-Context Learning is applicable. We
demonstrate that our method yields strong performance across a range of tasks,
standing up well when compared to existing methods.

在大语言模型时代，人机交互朝着自然语言发展，提供了前所未有的灵活性。然而，大语言模型在上下文学习领域内高效运行往往依赖于结构良好的提示。为了解决这一挑战，我们的研究提出了一个名为自动上下文学习的通用框架。在接收到用户的请求后，我们要求模型自主生成示例，包括标签、说明或推理路径。然后，模型利用这个自产的上下文来解决给定的问题。我们的方法具有普适性，可在适用于普通上下文学习的任何环境中实施。我们展示了我们的方法在一系列任务中取得了强大的性能，并与现有方法相比表现出色。