Which scenario best exemplifies the use of one-shot prompting?
A) Providing a detailed list of instructions followed by multiple examples
B) Giving one example of a complex task and expecting the model to generalize
C) Using a large dataset to train the model incrementally
D) Setting up a reinforcement learning environment with numerous iterations
Answer: B) Giving one example of a complex task and expecting the model to generalize
Explanation:
In one-shot prompting, we provide a single example of a complex task and expect the model to provide a required response. This is a technique used in machine learning where the model is given minimal examples to understand and perform the task. Unlike few-shot learning (which involves several examples) or traditional machine learning approaches (which involve large datasets), one-shot learning relies on the model’s ability to derive patterns and apply them with minimal input. This method is particularly useful in scenarios where it is impractical to gather large amounts of data.
In summary, One-shot prompting is a machine learning technique where the model is trained or prompted to perform a task by providing just one example.
Examples of One-shot Prompting:
- Example 1: If you want a model to recognize handwritten digits, you provide just one image of a handwritten digit (like the digit “3”) and expect the model to generalize and recognize other handwritten “3”s.
- Example 2: To generate a story about a robot, you might prompt the model with a single example sentence, such as “Once upon a time, there was a robot who dreamed of becoming human,” and then expect the model to generate a coherent story based on this one example.
Why the other options are wrong?
- A) Providing a detailed list of instructions followed by multiple examples: This describes few-shot prompting rather than one-shot prompting.
- C) Using a large dataset to train the model incrementally: This describes traditional machine learning approaches, not one-shot prompting.
- D) Setting up a reinforcement learning environment with numerous iterations: This is a reinforcement learning approach, which is different from one-shot learning.
Hence, option B is correct.
You may also go through series of MCQs/Quizzes on Prompt Engineering.