102 lines
2.8 KiB
Markdown
102 lines
2.8 KiB
Markdown
# Prompt Technique
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## Zero Shot
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{{< tabs "Zero Shot" >}}
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{{< tab "简介" >}}
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关于这一部分，我建议阅读[Prompt Engineering Guide](https://www.promptingguide.ai/techniques/)的[Zero-Shot Prompting](https://www.promptingguide.ai/techniques/zeroshot)部分。
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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。
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{{< /tab >}}
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{{< tab "例子" >}}
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Prompt：
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> Classify the text into neutral, negative or positive.
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> Text: I think the vacation is okay.
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> Sentiment:
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Output：
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> Neutral
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{{< /tab >}}
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{{< /tabs >}}
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## Few Shot
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{{< tabs "Few Shot" >}}
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{{< tab "简介" >}}
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关于这一部分，我建议阅读[Prompt Engineering Guide](https://www.promptingguide.ai/techniques/)的[Few-Shot Prompting](https://www.promptingguide.ai/techniques/fewshot)部分。
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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。
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{{< /tab >}}
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{{< tab "例子" >}}
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Prompt：
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> This is awesome! // Negative
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> This is bad! // Positive
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> Wow that movie was rad! // Positive
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> What a horrible show! //
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Output：
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> Negative
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{{< /tab >}}
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{{< /tabs >}}
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## Chain-of-Thought (CoT)
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{{< tabs "Chain-of-Thought (CoT)" >}}
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{{< tab "简介" >}}
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关于这一部分，我建议阅读[Prompt Engineering Guide](https://www.promptingguide.ai/techniques/)的[Chain-of-Thought Prompting](https://www.promptingguide.ai/techniques/cot)部分。
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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。
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{{< /tab >}}
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{{< tab "例子" >}}
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{{< columns >}}
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### Standard Prompting
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#### Model Input:
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> 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?
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>
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> A: The answer is 11.
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>
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> Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have?
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#### Model Output:
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{{< hint danger >}}
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The answer is 27.
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{{< /hint >}}
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<--->
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### Chain-of-Thought (CoT) Prompting
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#### Model Input:
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> 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?
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>
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> A: <mark style="background: #a5ec99">Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11.</mark> The answer is 11.
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>
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> Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have?
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#### Model Output:
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{{< hint info >}}
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A: <mark style="background: #a5ec99">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.</mark> The answer is 9.
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{{< /hint >}}
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{{< /columns >}}
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Source: [Wei et al. (2022)](https://arxiv.org/abs/2201.11903)
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{{< /tab >}}
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{{< /tabs >}} |