You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
23 lines
1.4 KiB
Markdown
23 lines
1.4 KiB
Markdown
# Zero-Shot Prompting
|
|
Large LLMs today, such as GPT-3, are tuned to follow instructions and are trained on large amounts of data; so they are capable of performing some tasks "zero-shot."
|
|
|
|
We tried a few zero-shot examples in the previous section. Here is one of the examples we used:
|
|
|
|
*Prompt:*
|
|
```
|
|
Classify the text into neutral, negative or positive.
|
|
|
|
Text: I think the vacation is okay.
|
|
Sentiment:
|
|
```
|
|
|
|
*Output:*
|
|
```
|
|
Neutral
|
|
```
|
|
|
|
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.
|
|
|
|
Instruction tuning has shown to improve zero-shot learning [Wei et al. (2022)](https://arxiv.org/pdf/2109.01652.pdf). Instruction tuning is essentially the concept of finetuning models on datasets described via instructions. Furthermore, [RLHF](https://arxiv.org/abs/1706.03741) (reinforcement learning from human feedback) has been adopted to scale instruction tuning wherein the model is aligned to better fit human preferences. This recent development powers models like ChatGPT. We will discuss all these approaches and methods in upcoming sections.
|
|
|
|
When zero-shot doesn't work, it's recommended to provide demonstrations or examples in the prompt which leads to few-shot prompting. In the next section, we demonstrate few-shot prompting. |