• While large-language models demonstrate remarkable zero-shot capabilities, they still fall short on more complex tasks when using the zero-shot setting.
• Few-shot prompting can be used as a technique to enable in-context learning where we provide demonstrations in the prompt to steer the model to better performance.
• The demonstrations / examples can potentially lead to significantly improved results, as the model has a broader understanding of the task it needs to perform.

In the above example, since the word “farduddle” is not from English Vocabulary and hence the Pre-Trained model has never been exposed to such a word, we need to give the model a reference (as an example) on how to use the such words in a sentence. 

 For the User Query to calculate the ROI, Zero-Shot Prompt gave an irrelevant response whereas a Multi-Shot Prompt with 2 examples (similar to the user-query) gave a correct response. Providing relevant reference to the model to solve a mathematical problem helped to get the correct response.

 To learn more about efficient Zero-Shot Prompting, refer my blog: Click Here

 Objective in the above example : Classify the Sentiment of a Product review as Positive, Negative or Neutral. 

 One-Shot Prompting 

Wrong Response : Model could not learn efficiently from just one example of a Positive review given as a reference to the model. 

 Few-Shot Prompting 

Correct Response : Model learnt better with a reference of 3 examples w.r.t each of the three classes. 

Scalability and Practicality

• Providing examples for highly specialized every possible task or variation can be impractical.
• For tasks involving complex legal language or technical jargon, creating relevant examples can be time-consuming and require domain expertise. 

Overfitting to Example

• The model might overfit to the given examples, meaning it performs well on similar inputs but poorly on slightly different ones.

Context Length Limitation

• Language models have a maximum context length they can handle. Providing too many examples might exceed this limit, truncating the input and losing critical information.