Zero shot , One shot and Few shot learning with examples

In-context learning (ICL) is a transformative approach in the field of machine learning that allows models to perform tasks with a minimal amount of training examples. Unlike traditional machine learning methods, which require vast datasets to learn from, in-context learning utilizes the inherent knowledge of pre-trained models to make sense of new tasks. This approach is particularly prevalent in language models such as GPT (Generative Pre-trained Transformer), where the ability to understand language nuances with little to no additional examples is a game-changer.

Zero-Shot Learning: Intuition Without Examples

Zero-shot learning is akin to a student correctly answering a question on a subject they’ve never been taught. In AI, this translates to a model performing a task without any prior specific examples. The model uses its pre-training on a diverse array of topics and structures to infer the correct response to a new prompt.

For example, when asked to classify the sentiment of the statement “I loved this movie!” without any prior examples, a zero-shot learning model uses its understanding of language to determine that the sentiment is positive.

One-Shot Learning: Learning From a Single Example

One-shot learning steps up the game by providing the model with a single example from which to learn. This single instance serves as a template for the model to follow. When the model encounters a similar task, it references the given example and applies the learned pattern to the new input.

In one-shot learning, if the model is shown that “I loved this movie!” corresponds to a positive sentiment, it can then extrapolate this understanding to a different statement such as “I don’t like this chair,” identifying the sentiment as negative.

Few-Shot Learning: Quick Adaptation with Few Examples

Few-shot learning strikes a balance between zero and one-shot learning by giving the model a handful of examples to learn from. This small dataset quickly brings the model up to speed on the specific nuances of a task, allowing for a more refined performance.

In the context of sentiment analysis, a model given a few examples with corresponding sentiments can more accurately classify new statements. It sees “I loved this movie!” as positive, “I don’t like this chair.” as negative, and then is asked to classify “Who would use this product?” Using the sentiments learned from the examples, the model can better understand the implied sentiment behind the new statement.

Summary of what we have discussed so far is as shown in the table given below :

Free Hit :

Why it is called ‘shot’ ?

The term “shot” in the context of zero-shot, one-shot, and few-shot learning is derived from the field of computer vision, where “shot” typically refers to the number of examples (or “shots”) of a given class that are provided to a model during training. Here’s why each term is used:

Zero-Shot Learning

• Why it’s called “Zero-Shot”: The term “zero-shot” is used because the model is given zero examples of the specific task during training. It must rely entirely on its pre-trained knowledge to perform the new task. It’s as if the model is taking a “shot in the dark” at solving a problem it has not seen before.

One-Shot Learning

• Why it’s called “One-Shot”: This term is used when the model is provided with only one example of the task. It has one “shot” or one chance to learn from a single instance. The model must generalize from this one example to understand and perform similar tasks.

Few-Shot Learning

• Why it’s called “Few-Shot”: In this case, “few” means more than one but significantly less than would typically be used in a machine learning context. The model has a “few shots” to learn from, which means it has only a small number of examples to guide its learning process for a specific task.

The “shot” terminology is a metaphorical way of referring to the quantity of exposure the model has to a particular type of data or task during its training phase, before it is tested on its ability to perform that task.