Few-Shot Prompting

What it is about:

Few-shot prompting is a technique for improving the performance of large language models (LLMs) on complex tasks by providing a few examples within the prompt itself. While zero-shot prompting relies solely on the LLM's pre-trained knowledge, few-shot prompting offers a middle ground by providing additional guidance through demonstrations.

How it works:

  • Instructing the LLM: The prompt clearly defines the task at hand and what the LLM needs to accomplish.
  • Demonstrating Examples: The prompt includes a small set of examples (typically 1-shot, 3-shot, 5-shot, etc.) that showcase how to complete the task correctly. These examples can be in the form of question-answer pairs, sentences with specific word usage, or any format relevant to the task.
  • Learning from Examples: The LLM analyzes the provided examples to understand the expected response format, reasoning steps involved, and the relationship between input and output.
  • Applying the Learned Pattern: Based on the examples, the LLM attempts to generalize the pattern and apply it to new, unseen examples presented after the demonstrations.

Example:

Task: Learn to use a new word correctly in a sentence.

Prompt:

A "wug" is a small, furry animal native to Australia. An example of a sentence that uses the word wug is: "We saw a wug at the zoo." Write a sentence using the word "wug."

Output:

"The children played with the wugs in the park."

Here, the model learns how to use the word "wug" in a sentence by analyzing the provided example.


When to use it:

Few-shot prompting is a valuable approach for tasks where:

  • Zero-shot prompting is insufficient: The task requires more guidance than what the LLM's pre-trained knowledge can provide.
  • Fine-tuning is not feasible: Limited resources or time constraints make fine-tuning impractical.
  • Examples are readily available: You can easily create a small set of relevant examples to demonstrate the desired behavior.

Conclusion:

Few-shot prompting bridges the gap between zero-shot prompting and fine-tuning, offering a practical way to enhance LLM performance for various tasks. However, the effectiveness of this technique depends on the complexity of the task and the quality of the provided examples.