Few-Shot Learning with OpenAI's ChatGPT, A Comparative Study of Methods

In the world of machine learning and natural language processing, OpenAI’s ChatGPT has made waves with its incredible language comprehension and generation capabilities. One of the striking features of ChatGPT is its ability to do “few-shot learning.” In simple terms, this means that the model can be guided to perform a specific task by showing it a few examples (or “shots”) of that task beforehand.

But what’s the best way to implement few-shot learning? Two popular approaches exist: one involving a single prompt with examples, and another leveraging the conversation history with the API. Let’s delve into these two methods to see how they work and how they compare.

Method 1: Single Prompt with Few-Shots

The first approach involves creating a single, concatenated prompt that includes the examples and the new input for which you want an output. You structure the prompt so that it establishes a pattern for the model to follow. The format might look something like this:

Translate the following English to French phrases:  
- Hello -> Bonjour  
- Thank you -> Merci  
Translate: Good morning

When passed to the API, the model recognizes the pattern you’ve provided in the input and attempts to follow it in the output, translating “Good morning” to French, likely as “Bonjour.”

Pros:

Simple to implement.
No need to manage conversation history.

Cons:

Less dynamic; doesn’t adapt based on interactive feedback.
Might require more tokens, leaving less room for the actual task.

Method 2: Conversation Style Few-Shots

The second approach uses the conversation history as the method for providing examples. Each message in the history serves as a “shot,” teaching the model the desired behavior. You can communicate with the model via the API by passing a list of message objects. Each object typically has a “role” (either “user” or “assistant”) and “content” (the text of the message).

Here’s how the conversation could be structured in Python when using the API:

conversation = [
    {"role": "user", "content": "Translate the following English to French: Hello"},
    {"role": "assistant", "content": "Bonjour"},
    {"role": "user", "content": "Translate: Thank you"},
    {"role": "assistant", "content": "Merci"},
    {"role": "user", "content": "Translate: Good morning"}
]

Pros:

More dynamic; allows you to interactively adapt and refine your input.
Uses fewer tokens for examples, leaving more space for additional interactions.

Cons:

Requires managing the conversation history.

Comparison and Conclusion

While both methods can be effective, your choice may depend on the specific task and how you plan to interact with the model. If you need a one-off, non-interactive task, the single-prompt method may suffice. However, if you’re developing a more interactive application where the context and examples may change over time, the conversational approach could offer more flexibility.

Regardless of the method you choose, few-shot learning can be a powerful way to guide ChatGPT in generating the output you desire, making it a more valuable tool for a wide variety of tasks.