基于当前上下文中的指令和示例对模型进行条件化，而不更新模型参数
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模式： 在提示中提供任务示例，使模型无需更新参数即可学习
原因： 实现快速任务适配，无需训练，利用模型的模式识别能力立即产出效果
关键洞察： 模型可从上下文中的示范中学习，在推理时执行隐式梯度下降
通过这些精选资源加深理解
Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent (Dai et al., 2022)
Transformers Learn In-Context by Gradient Descent (von Oswald et al., 2022)
In-context Learning and Induction Heads (Olsson et al., 2022)
What learning algorithm is in-context learning? Investigations with linear models (Akyürek et al., 2022)
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models (Zhou et al., 2022)
Self-Consistency Improves Chain of Thought Reasoning in Language Models (Wang et al., 2022)
Large Language Models are Zero-Shot Reasoners (Kojima et al., 2022)
Measuring and Narrowing the Compositionality Gap in Language Models (Press et al., 2022)
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? (Min et al., 2022)
Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations (Yoo et al., 2022)
Fantastically Ordered Prompts and Where to Find Them (Lu et al., 2021)