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图解大模型 : 生成式AI 原理与实战
book

图解大模型 : 生成式AI 原理与实战

by Jay Alammar, Maarten Grootendorst
May 2025
Intermediate to advanced
382 pages
10h 33m
Chinese
Posts & Telecom Press
Content preview from 图解大模型 : 生成式AI 原理与实战
提示工程
145
Why don't chickens make good comedians? Because their 'jokes' always 'feather'
the truth!
如表
6-1
所示,这些参数允许用户在创造性(高
temperature
和高
top_p
)和可预测性(低
temperature
和低
top_p
)之间进行调节。
6-1:选择
temperature
top_p
值的用例
示例应用场景
temperature top_p
描  述
头脑风暴会议 高随机性输出,且可能输出的词元集合较大。生成的结果
通常高度多样化,往往富有创意和出人意料
邮件生成 高确定性的输出,且可能输出的词元集合较小。这会产生
可预测、重点明确和保守的输出
创意写作 高随机性输出,但可能输出的词元集合较小。这会产生有
创意的输出,但仍保持连贯性
翻译 高确定性的输出,但可能输出的词元集合较大。这会产生
连贯的输出,并且具有更广泛的词汇范围,从而更具语言
多样性
6.2
 提示工程简介
在使用文本生成类
LLM
的过程中,提示工程是一个至关重要的部分。通过精心设计提示
词,我们可以引导
LLM
生成所需的响应。无论提示词是问题、陈述还是指令,提示工程
的主要目标都是引导模型生成有用的回复。
提示工程不仅仅是设计效果良好的提示词这么简单。它可以用作评估模型输出的工具,也
可用于设计保障措施和风险控制方法。提示词优化的迭代过程需要不断实验。目前没有,
未来也不太可能有完美的提示词设计。
在本节中,我们将介绍提示工程的常用方法,以及一些小技巧 ...
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Publisher Resources

ISBN: 9787115670830