What is In-Context Learning?
A method where models are guided to perform tasks using examples provided in the input prompt.
More about In-Context Learning:
In-Context Learning enables AI models to perform tasks by including examples or context directly in the input prompt. This approach leverages pre-trained models like PLMs to adapt to new tasks without additional fine-tuning.
In-context learning is particularly effective for tasks like question answering, prompt engineering, and retrieval fusion, where contextual examples guide model behavior.
Frequently Asked Questions
How does in-context learning differ from few-shot learning?
In-context learning provides examples in the input prompt, while few-shot learning involves fine-tuning with minimal labeled data.
What tasks are well-suited for in-context learning?
Tasks like semantic search, context-aware generation, and dialogue systems benefit greatly from in-context learning.
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