Few-Shot Learning in NLP: Making AI Smarter with Less Data

Few-shot learning is revolutionizing AI by allowing models to learn new tasks blazingly fast with just a handful of examples.
Illustration showing few-shot learning in NLP where an AI model learns from limited data examples like text, images, and code.

Last Updated on April 27, 2024 9:59 PM IST

In the recent advances in natural language processing (NLP), a new trend has emerged. It is known as few-shot learning in NLP.

Basically, it is a technique in which machines learn to do tasks with limited training. This helps in performing challenging NLP tasks such as question answering, text classification, comprehensive reading, and much more. Isn’t it cool?

So, in today’s post, we will deep dive into and understand how smart few-shot learning is. Thus, we will look at how these smart algorithms achieve great results with minimal data, which opens a new door of possibilities in artificial intelligence. So, let us start learning.

What is few-shot learning in NLP?

Few-shot learning is a technique that allows AI models to learn from limited data or minimal examples. This will help the large language models to generalize new situations. It is an ML method that is very popular in models like ChatGPT.

It helps models learn from a few examples, allowing them to adapt quickly to new tasks or scenarios with minimal data.

This approach is transforming AI by reducing the need for vast amounts of training data. This helps the AI model to mimic how humans acquire new skills or knowledge with a small number of instructions or observations.

Let’s understand it with an example. If you see a picture of a dolphin and read a bit about it, then you will be able to recognize it in other pictures or descriptions in the future, even if you haven’t seen it before. It also uses transfer learning while performing a target task.

The key idea behind this is to teach models to learn quickly and efficiently with a limited number or with few shots of examples.

The overall aim is to help the AI model understand underlying patterns, similarities, or concepts with data scarcity.

Thus, it helps models make accurate predictions or classifications based on new and unseen data points.

How does it work?

There are various approaches to few-shot learning. But pre-training and fine-tuning are the most popular ones.

Pre-training consists of training data on a massive and diverse dataset, like social media, world news or Wikipedia.

This helps the model learn a lot about the language, its structure, and general knowledge about the world.

The second common approach is fine-tuning; it will adapt the pre-trained model for a specific task or domain. It will just update its parameters along with limited support examples.

Thus, with prior knowledge and support set, the model can get better at performing new tasks.

Few-Shot Learning Techniques

After understanding about few-shot learning and how it works, let us look at the techniques that help it.

Metric-Based Approaches

The metric-based approach consists of two networking techniques, i.e., Siamese networks and prototypical networks.

Siamese Networks

Siamese networks help the model learn and create special representations (embeddings) for input data. Then it uses distance metrics to check how similar these representations are; this will help in comparing and classifying things based on similarity.

In simple terms, Siamese networks help to understand semantic similarity. Imagine having a set of labelled examples for several categories (like fruits).

The Siamese network will learn how to compare new unlabelled examples, such as pictures of fruits, and understand which category they belong to.

In simple terms, it is like teaching the network to recognize the similarities and differences between things. This helps to classify new things into the right categories based on their learning.

Prototypical networks

A prototypical network is a smart way to perform few-shot learning. In this technique, it will find the “average” for every category, which is known as the prototype.

When there is a new example, the network will check how similar it is to these prototypes and figure out to which category it belongs.

For example, imagine having pictures of various birds, such as pigeons and owls. The network will then calculate the average of how a pigeon and owl look based on the annotated examples it has.

When a new picture comes in, it will compare it to these average pigeons and owls and decide whether it’s a pigeon or an owl.

Prototypical networks are fast, and they don’t require any special techniques to work well. And this makes it the best. Hence, it is very popular in areas like computer vision.

Model-Based Approaches

In the next section, we will look at model-based approaches, which play a key role in improving the model’s performance.

Memory-Augmented Networks

Memory-augmented networks (MANNs) are updated versions of neural networks and offer better performance.

They have a special “memory” component that can remember important details with a few examples.

This memory helps them to handle large-scale tasks that traditional neural networks struggle with.

For example, you have to remember a complex recipe with several steps. Regular neural networks might get confused or loss of function can take place. But MANNs will jot down the key pointers in their memory.

And, when they need to solve a problem, such as identifying an object in a picture, they can refer back to what they’ve remembered

This allows autonomous systems to make better decisions based on what they’ve learned previously. It’s similar to having a notepad to jot down important clues and refer back to them as needed.


Meta-learning helps improve the model’s performance by training models to learn quickly from previous tasks.

They apply this knowledge to new ones during the meta-training phase. One of the main components is to extract “meta-knowledge” or prior experience from training so that the model can quickly adapt to new, unfamiliar tasks during meta-testing. It’s like teaching the model how to learn effectively from its own learning experiences.

Gradient-Based Meta-Learning

Gradient-based meta-learning will adjust the model parameters, which will help in model training during meta-testing.

The primary goal of MAML is to enable models to adapt quickly to new tasks with few examples of class labels, which is critical in meta-learning scenarios.

In simple terms, it’s like teaching a computer model to be super good at quickly learning new stuff with minimal practice.

Application and Use Cases

The results demonstrated by GPT-2 suggest various applications. For example, in zero and one-shot settings, GPT-3 has offered amazing results on various tasks.

For example, OpenAI’s GPT-3, which is among the largest and most powerful language models, can perform various NLP tasks such as text generation, classification, summarization, sentiment analysis and machine translation with just a few examples or prompts as inputs.

The second example is Google’s T5, which is a text-to-text model that can convert any input text to output text, such as converting questions into answers or languages such as English into German, with just a few examples or prompts.

Another popular example is Facebook’s XLM, which is cross-lingual and can be used to generate text across 100 languages with a few examples or labels in any language.

For a deeper understanding of prompt engineering techniques, you can refer to this prompt engineering cheat sheet.

Benefits and limitations

After looking at few-shot learning and its application, let us navigate towards its benefits and limitations of it.

Data Efficiency: Few-shot learning is highly data-efficient because it needs only a few labelled examples. This is extremely useful when getting massive labelled datasets, which is expensive or not possible.

Generalization to New Tasks: Few-shot learning models are excelling at adapting new tasks or classes with minimal labelled data. They are extremely flexible and can handle new data well, which makes them perfect for a constantly changing environment.

Reduced Manual Labelling: Few-shot learning minimizes the need for manual data labelling because it requires few labelled examples while training. This saves time and resources that were spent on annotation efforts.

Limited Class Discrimination: In few-shot learning, there might not be sufficient examples to capture minor differences between closely related classes. This can lead to a reduced ability while distinguishing between classes.

Model Design Complexity: Developing good few-shot learning models requires complex designs and training methods, which can be challenging.

To explore more about the benefits of NLP, check out this insightful article on the Top 8 Benefits of Natural Language Processing (NLP).

Summing Up

In conclusion, few-shot learning in NLP is a revolution in the field of artificial intelligence and natural language processing.

This technique helps machines to learn with a small amount of data, which makes them incredibly efficient and adaptable. It has shown amazing results in tasks such as question answering, text generation, and cross-lingual understanding.

It also consists of several benefits along with some challenges, but with the development of AI technology and continuous future research, few-shot learning has the potential to open up new frontiers in AI, making machines smarter and more capable of dealing with diverse and evolving tasks.

In the end, stay connected with Appskite to experience the power of AI.


Can few-shot learning techniques be applied across different domains, or are they primarily effective only for NLP tasks?

Few-shot learning techniques are not limited to NLP-related tasks; they are also effective in various data science domains such as image classification tasks and computer vision. For example, Siamese networks and prototypical networks can be used for visual and text data, which allows models to identify similarities and image classifications with limited labelled examples.

How are few-shot learning models better than traditional machine-learning models in terms of accuracy and computational efficiency?

Few-shot learning models frequently achieve comparable or better accuracy than traditional machine learning models, particularly in scenarios with limited labelled data. This feature enhances Few-Shot Learning’s ability to provide better results from small training sets, which makes it useful in tasks such as image recognition where labelled data is scarce.

Which industry sectors benefit the most from few-shot learning techniques?

Few-shot learning in NLP has applications in various domains, including deep learning, healthcare, robotics, and machine learning. In healthcare, it helps in the medical diagnosis of rare diseases by leveraging limited labelled data, thereby improving patient care. Simultaneously, robotics enables rapid task learning, which improves automation in industries such as manufacturing and logistics.

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