Mastering ChatGPT Prompts: Harnessing Zero, One, and Few-Shot Learning, Fine-Tuning, and Embeddings for Enhanced GPT Performance

Lately, I've been getting many questions about how I create my complex prompts for ChatGPT and OpenAi API. This is a summary of what I've learned.

Zero-shot, one-shot, and few-shot learning refers to how an AI model like GPT can learn to perform a task with varying amounts of labelled training data. The ability of these models to generalize from their pre-training on large-scale datasets allows them to perform tasks without task-specific training.

Prompt Types & Learning

Zero-shot learning: In zero-shot learning, the model is not provided any labelled examples for a specific task during training but is expected to perform well on it. This is achieved by leveraging the model's pre-existing knowledge and understanding of language, which it gained during the general training process. GPT models are known for their ability to perform reasonably well on various tasks with zero-shot learning.

Example: You ask GPT to translate an English sentence to French without providing any translation examples. GPT uses its general understanding of both languages to generate a translation.

Prompt: "Translate the following English sentence to French: 'The cat is sitting on the mat.'"

One-shot learning: In one-shot learning, the model is provided with a single labeled example for a specific task, which it uses to understand the nature of the task and generate correct outputs for similar instances. This approach can be used to incorporate external data by providing an example from the external source.

Example: You provide GPT with a single example of a translation between English and French and then ask it to translate another sentence.

Prompt: "Translate the following sentences to French. Example: 'The dog is playing in the garden.' -> 'Le chien joue dans le jardin.' Translate: 'The cat is sitting on the mat.'"

Few-shot learning: In few-shot learning, the model is provided with a small number of labeled examples for a specific task. These examples help the model better understand the task and improve its performance on the target task. This approach can also include external data by providing multiple examples from the external source.

Example: You provide GPT with a few examples of translations between English and French and then ask it to translate another sentence.

Prompt: "Translate the following sentences to French. Example 1: 'The dog is playing in the garden.' -> 'Le chien joue dans le jardin.' Example 2: 'She is reading a book.' -> 'Elle lit un livre.' Example 3: 'They are going to the market.' -> 'Ils vont au marché.' Translate: 'The cat is sitting on the mat.'"

Fine Tuning

For specific tasks or when higher accuracy is required, GPT models can be fine-tuned with more examples to perform better. Fine-tuning involves additional training on labelled data particular to the task, helping the model adapt and improve its performance. However, GPT models may sometimes generate incorrect or nonsensical answers, and their performance can vary depending on the task and the amount of provided examples.

Embeddings

An alternative approach to using GPT models for tasks is to use embeddings. Embeddings are continuous vector representations of words or phrases that capture their meanings and relationships in a lower-dimensional space. These embeddings can be used in various machine learning models to perform tasks such as classification, clustering, or translation by comparing and manipulating the embeddings. The main advantage of using embeddings is that they can often provide a more efficient way of handling and representing textual data, making them suitable for tasks where computational resources are limited.

Including External Data

Incorporating external data into your AI model's training process can significantly enhance its performance on specific tasks. To include external data, you can fine-tune the model with a task-specific dataset or provide examples from the external source within your one-shot or few-shot learning prompts. For fine-tuning, you would need to preprocess and convert the external data into a format suitable for the model and then train the model on this data for a specified number of iterations. This additional training helps the model adapt to the new information and improve its performance on the target task.

If not, you can also directly supply examples from the external dataset within your prompts when using one-shot or few-shot learning. This way, the model leverages its generalized knowledge and the given examples to provide a better response, effectively utilizing the external data without explicit fine-tuning.

A Few Final Thoughts

1. Task understanding and prompt formulation: The quality of the generated response depends on how well the model understands the prompt and its intention. A well-crafted prompt can help the model to provide better responses.
2. Limitations of embeddings: While embeddings offer advantages in terms of efficiency, they may not always capture the full context and nuances of the text. This can result in lower performance for certain tasks compared to using the full capabilities of GPT models.
3. Transfer learning: It is worth mentioning that the generalization abilities of GPT models are the result of transfer learning. During pre-training, the model learns to generate and understand the text by predicting the next word in a sequence. This learned knowledge is then transferred to other tasks, even if they are not explicitly trained on these tasks.

Example Prompt

Here's an example of a few-shot learning task using external data in JSON format. The task is to classify movie reviews as positive or negative:

  "task": "Sentiment analysis",
  "examples": [
    {
      "text": "The cinematography was breathtaking and the acting was top-notch.",
      "label": "positive"
    },
    {
      "text": "I've never been so bored during a movie, I couldn't wait for it to end.",
      "label": "negative"
    },
    {
      "text": "A heartwarming story with a powerful message.",
      "label": "positive"
    },
    {
      "text": "The plot was confusing and the characters were uninteresting.",
      "label": "negative"
    }
  ],
  "external_data": [
    {
      "text": "An absolute masterpiece with stunning visuals and a brilliant screenplay.",
      "label": "positive"
    },
    {
      "text": "The movie was predictable, and the acting felt forced.",
      "label": "negative"
    }
  ],
  "new_instance": "The special effects were impressive, but the storyline was lackluster."
}{        

To use this JSON data in a few-shot learning prompt, you can include the examples from both the "examples" and "external_data" fields:

Based on the following movie reviews and their sentiment labels, determine if the new review is positive or negative.

Example 1: "The cinematography was breathtaking and the acting was top-notch." -> positiv
Example 2: "I've never been so bored during a movie, I couldn't wait for it to end." -> negative
Example 3: "A heartwarming story with a powerful message." -> positive
Example 4: "The plot was confusing and the characters were uninteresting." -> negative
External Data 1: "An absolute masterpiece with stunning visuals and a brilliant screenplay." -> positive
External Data 2: "The movie was predictable, and the acting felt forced." -> negative

New review: "The special effects were impressive, but the storyline was lackluster."e