Zero Shot Prompting

Zero-shot prompting means that the prompt used to interact with the model won't contain examples or demonstrations. The zero-shot prompt directly instructs the model to perform a task without any additional examples to steer it.

Prompt:

Output:

Note that in the prompt above, we didn't provide the model with any examples of text alongside their classifications; the LLM already understands "sentiment" - that's the zero-shot capabilities at work.

When zero-shot doesn't work, it's recommended to provide demonstrations or examples in the prompt, which leads to few-shot prompting.

Instruction tuning has been shown to improve zero-shot learning (see this research paper for more info). Instruction tuning is essentially the concept of fine-tuning models on datasets described via instructions. Furthermore, RLHF (reinforcement learning from human feedback) has been adopted to scale instruction tuning, wherein the model is aligned to better fit human preferences.