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In-context learning can be categorized into zero-shot, one-shot, and few-shot settings based on the number of task-specific input-output examples provided in the prompt. Zero-shot involves no examples, one-shot involves exactly one, and few-shot provides a few examples, all of which guide the Transformer decoder to perform tasks without any parameter updates.

Zero-Shot, One-Shot, and Few-Shot Learning Settings

A developer is experimenting with three different prompts to have a language model classify customer feedback. Analyze the three prompts below. For each prompt, identify whether it is an example of a zero-shot, one-shot, or few-shot approach and briefly justify your classification based on the number of examples provided.

Prompting Strategy Identification

A developer is constructing prompts to guide a language model for different tasks. Match each prompting approach to the description of the prompt's structure.

A developer is using a large language model to perform a novel task: converting movie titles into short, rhyming poems. Their initial attempts, which only provide the instruction 'Turn this movie title into a rhyming poem,' are yielding inconsistent and low-quality results. Which of the following strategies is most likely to improve the model's performance for this specific creative task?

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