Differences between Zero-Shot and Few-Shot Prompting

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Zero-Shot and Few-Shot Prompting

Below are some of the differences between Zero-Shot and Few-Shot Prompting techniques:

Aspect	Zero-Shot Prompting	Few-Shot Prompting
Definition	The model is prompted to perform a task without any prior examples or specific instructions.	The model is provided with a few examples to guide its response.
Examples Provided	No examples are given to the model.	A few examples of input-output pairs are provided to the model.
Task Familiarity	The model needs to understand and perform a task it hasn’t seen before, relying on its prior knowledge.	The model is shown examples of the task, helping it learn from them to provide a better response.
Use Case	Used when the task is simple, or when you want the model to generalize based on its training.	Used when the task requires more guidance or context to ensure accurate responses.
Dependency on Training Data	Relies entirely on the model’s pre-existing knowledge from training data.	Relies on examples to help the model understand the task context and improve its response.
Response Accuracy	Responses may vary in quality depending on how well the task aligns with the model’s training.	Typically more accurate for specific tasks, as the model has examples to refer to.
Flexibility	More flexible as the model can attempt to handle any prompt, even without explicit task training.	Less flexible as the model depends on the examples provided to perform the task.
Complexity	Less complex, as it does not require providing examples for each specific task.	More complex, as providing examples requires preparation and understanding of the task.
Examples	“Translate ‘hello’ to French” without any example provided.	“Translate ‘hello’ to French: ‘Hola’ → ‘Bonjour’, ‘goodbye’ → ‘Au revoir’, ‘hello’ → ?”
Response Consistency	May produce less consistent or varied answers due to the lack of guidance.	Typically produces more consistent responses due to the guidance from examples.