In-Context Learning

In-context learning refers to the ability of a model, particularly in natural language processing, to learn and adapt to tasks based on the context provided within the input data. Instead of requiring extensive retraining, the model can adjust its responses based on examples or prompts given at inference time. This characteristic allows for flexibility and rapid adaptation to new tasks without the need for additional training data. Common use cases include question answering, text generation, and conversational agents, where the model leverages context to produce relevant outputs. In-context learning showcases the potential of large language models to understand and perform a variety of tasks with minimal explicit training.