Zero-Shot Learning, Instruction Tuning, and Few-Shot Prompting in Large Language Models

Nowadays, Large Language Models (LLMs) trained on massive datasets and fine-tuned via instruction tuning are capable of performing zero-shot tasks. We explored some zero-shot examples in the previous section, and here’s one case we used:

Prompt:

plaintext

`Classify the text as neutral, negative, or positive.

Text: I think this holiday was okay.
Sentiment:`

Output:

plaintext

neutral

Note that in the prompt above, we did not provide any examples to the model—this exemplifies the role of zero-shot capabilities.

Instruction tuning has been proven to enhance zero-shot learning Wei et al. (2022). Essentially, instruction tuning involves fine-tuning models on datasets described by instructions. Additionally, RLHF (Reinforcement Learning from Human Feedback) has been adopted to extend instruction tuning, adjusting models to better align with human preferences. This recent advancement has driven the development of models like ChatGPT. We will discuss all these methods in subsequent chapters.

When zero-shot learning is ineffective, it is recommended to provide demonstrations or examples in the prompt, which leads to few-shot prompting. In the next section, we will demonstrate the application of few-shot prompting.