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In-Context Learning in Large Language Models

The ability of a large language model to perform a task from demonstrations placed in its input at inference time, without any weight updates, and the vocabulary of zero-shot, one-shot and few-shot settings that describes how many demonstrations are supplied.

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In-context Learning in Prompt Engineering using Few-Shot Examples to Activate Model Capabilities

In-context learning (ICL) is a theoretical framework in prompt engineering where large language models infer task-specific capabilities from external examples provided within the input context withou…