'Few-shot learning in practice: GPT-Neo and the 🤗 Accelerated Inference API'
philschmid
In many Machine Learning applications, the amount of available labeled data is a barrier to producing a high-performing model. The latest developments in NLP show that you can overcome this limitation by providing a few examples at inference time with a large language model - a technique known as Few-Shot Learning. In this blog post, we'll explain what Few-Shot Learning is, and explore how a large language model called GPT-Neo, and the 🤗 Accelerated Inference API, can be used to generate your own predictions.


## What is Few-Shot Learning?

Few-Shot Learning refers to the practice of feeding a machine learning model with a very small amount of training data to guide its predictions, like a few examples at inference time, as opposed to standard fine-tuning techniques which require a relatively large amount of training data for the pre-trained model to adapt to the desired task with accuracy.

This technique has been mostly used in computer vision, but with some of the latest Language Models, like [EleutherAI GPT-Neo](https://www.eleuther.ai/research/projects/gpt-neo/) and [OpenAI GPT-3](https://openai.com/blog/gpt-3-apps/), we can now use it in Natural Language Processing (NLP). 

In NLP, Few-Shot Learning can be used with Large Language Models, which have learned to perform a wide number of tasks implicitly during their pre-training on large text datasets. This enables the model to generalize, that is to understand related but previously unseen tasks, with just a few examples.

Few-Shot NLP examples consist of three main components: 

- **Task Description**: A short description of what the model should do, e.g. "Translate English to French"
- **Examples**: A few examples showing the model what it is expected to predict, e.g. "sea otter => loutre de mer"
- **Prompt**: The beginning of a new example, which the model should complete by generating the missing text, e.g. "cheese => "

![few-shot-prompt](assets/22_few_shot_learning_gpt_neo_and_inference_api/few-shot-prompt.png)  
<small>Image from <a href="https://arxiv.org/abs/2005.14165" target="_blank">Language Models are Few-Shot Learners</a></small>

Creating these few-shot examples can be tricky, since you need to articulate the “task” you want the model to perform through them. A common issue is that models, especially smaller ones, are very sensitive to the way the examples are written.

An approach to optimize Few-Shot Learning in production is to learn a common representation for a task and then train task-specific classifiers on top of this representation.

OpenAI showed in the [GPT-3 Paper](https://arxiv.org/abs/2005.14165) that the few-shot prompting ability improves with the number of language model parameters.

![few-shot-performance](assets/22_few_shot_learning_gpt_neo_and_inference_api/few-shot-performance.png)  
<small>Image from <a href="https://arxiv.org/abs/2005.14165" target="_blank">Language Models are Few-Shot Learners</a></small>

Let's now take a look at how at how GPT-Neo and the 🤗 Accelerated Inference API can be used to generate your own Few-Shot Learning predictions!