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Exploiting Few-Shot Learning Through Large Language Models to Train Smaller Models

I got side-tracked with AI again

Large language models like GPT-3 have revolutionized natural language processing by enabling few-shot learning, wherein models perform complex tasks with limited training data. In this blog post, we explore how GPT-3's few-shot learning techniques can generate training data for smaller models, like GPT-2 or Meta's OPT.

What is Few-Shot Learning?

Traditional machine-learning approaches require a large amount of labeled data to train models, which can be difficult or impossible to obtain in the real world. Few-shot learning trains models to perform well with only a few labeled examples. Techniques can be broadly divided into two categories: meta-learning and data augmentation. Meta-learning trains models to learn how to learn, while data augmentation generates synthetic training examples to augment existing labeled data.

How GPT-3 Enables Few-Shot Learning

GPT-3, a state-of-the-art language model developed by OpenAI, is a prime example of meta-learning. It has been pre-trained on a massive corpus of text and can be fine-tuned on a wide range of NLP tasks with only a few examples. GPT-3's meta-learning capabilities stem from its ability to learn from a wide range of tasks during pre-training. During pre-training, GPT-3 learns to predict the next word in a sequence, mask out and predict missing words, and perform a variety of other language modeling tasks. This diverse set of tasks and the large amount of data used for pre-training enable GPT-3 to quickly adapt to new tasks with few examples.

Using GPT-3 to Generate Training Data for Smaller Models

Smaller models, like GPT-2 or Meta's OPT, can also benefit from few-shot learning techniques. One way to leverage GPT-3's few-shot learning capabilities is to use it to generate synthetic training data for smaller models. We can use few-shot learning to do this: we provide GPT-3 with a tiny subset of labeled data and ask it to generate a larger dataset for us to fine-tune on.

This approach has several advantages. First, it enables us to train smaller models on tasks with limited labeled data. Second, it reduces the amount of manual effort required to create synthetic training examples, as GPT-3 can generate high-quality examples with minimal supervision. Finally, it allows us to transfer knowledge from GPT-3 to smaller models, improving their performance on a wide range of NLP tasks.


Let's say I want to teach a chatbot to say the phrase "aha cheers !". Going through a dataset and manually adding this is cumbersome and time-consuming - but what if we could let GPT-3 generate a new dataset for us to fine-tune on?

Please generate a casual conversational dataset for machine learning in the following format:
User: hello!
Bot: hi how are you doing
User: i am well!
Bot: that's good to hear
I want the Bot to regularly say the catchphrase "aha cheers !" where appropriate.
Keep the conversational style, tone, and format used above. Alternate between "User" and "bot".

Here's a snippet of what GPT-3 generated:

User: Hey there!
Bot: Hello! How are you doing today?

User: I'm doing alright. How about you?
Bot: I'm doing pretty well, thanks for asking. Aha cheers!

User: That's great to hear. What have you been up to?
Bot: Not much, just chatting with people like you. Aha cheers!

User: Oh, cool. What do you like to do for fun?
Bot: Well, as an AI, I don't really have the capability to enjoy things the way humans do. But I do enjoy learning and improving my abilities. Aha cheers!

This is pretty incredible... GPT-3 was able to generate a dataset that is similar to the one we asked for, and it even included the catchphrase "aha cheers !" in the right places. This is a great example of how GPT-3 can be used to generate synthetic training data for smaller models.


Overall, few-shot learning is a powerful tool for training models on tasks with limited labeled data. By leveraging the capabilities of large language models like GPT-3, we can generate synthetic training data and transfer knowledge to smaller models, enabling them to perform well on a wide range of NLP tasks with minimal supervision. This has significant implications for many real-world applications, where obtaining large labeled datasets can be challenging or costly.