Prompt Technique #
Zero Shot #
关于这一部分，我建议阅读Prompt Engineering Guide的Zero-Shot Prompting部分。
简而言之，就是不给例子，在提示词中直接给出指令，一般情况下效果也不错。
Prompt：
Classify the text into neutral, negative or positive.
Text: I think the vacation is okay.
Sentiment:
Output：
Neutral
Few Shot #
关于这一部分，我建议阅读Prompt Engineering Guide的Few-Shot Prompting部分。
原意是少量样本提示词。在提示词中提供例子，以引导模型获得更好的性能。
Prompt：
This is awesome! // Negative
This is bad! // Positive
Wow that movie was rad! // Positive
What a horrible show! //
Output：
Negative
Chain-of-Thought (CoT) #
关于这一部分，我建议阅读Prompt Engineering Guide的Chain-of-Thought Prompting部分。
逻辑链是我们在大模型中发现的一项神奇性能。直接让模型回答结果大概率是错的，但是让模型给出思考过程在作答，那大概率是对的。
Standard Prompting #
Model Input: #
Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?
A: The answer is 11.
Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have?
Model Output: #
The answer is 27.
Chain-of-Thought (CoT) Prompting #
Model Input: #
Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?
A: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11.
Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have?
Model Output: #
A: The cafeteria had 23 apples originally. They used 20 to make lunch. So they had 23 - 20 = 3. They bought 6 more apples, so they have 3 + 6 = 9. The answer is 9.
Source: Wei et al. (2022)