prompt trick
目的
通过模板使得预测任务与预训练模型的训练任务相统一，拉近预训练任务目标与下游微调目标的差距
和finetune差异
finetune：PTM向下兼容specific task
prompt：specific task向上兼容PTM
应用场景
由于其当前预测任务与预训练模型的训练任务相统一，所以我们可以在训练数据较少，甚至没有的情况下去完成当前任务，总结一下，其比较适合的应用场景：
- zero-shot
- few-shot
- 冷启动
Pre-train, Prompt, and Predict A Systematic Survey of Prompting Methods in Natural Language Processing
0 和pre-train，finetune区别
prompt感觉是一种特殊的finetune方式，还是先pre-train然后prompt tuning
目的：prompt narrowing the gap between pre-training and fine-tuning
1 怎么做
3步
1 Prompt Addition
$x^{‘}=f_{prompt}(x)$ x是input text
- Apply a template, which is a textual string that has two slots: an input slot [X] for input x and an answer slot
[Z] for an intermediate generated answer text z that will later be mapped into y. - Fill slot [X] with the input text x.
2 Answer Search
f：fills in the location [Z] in prompt $x^{‘}$ with the potential answer z
Z：a set of permissible values for z
3 Answer Mapping
因为上面的 $\hat{z}$ 还不是 $\hat{y}$，比如情感分析，“excellent”, “fabulous”, “wonderful” -》positive
go from the highest-scoring answer $\hat{z}$ to the highest-scoring output $\hat{y}$
4 举个例子，文本情感分类的任务
原来
“ I love this movie.” -》 positive
现在
1 $x=$ “ I love this movie.” -》模板为： “ [x] Overall, it was a [z] movie.” -》$x^{‘}$为”I love this movie. Overall ,it was a [z] movie.”
2 下一步会进行答案搜索，顾名思义就是LM寻找填在[z] 处可以使得分数最高的文本 $\hat{z}$(比如”excellent”, “great”, “wonderful” )
3 最后是答案映射。有时LM填充的文本并非任务需要的最终形式(最终为positive，上述为”excellent”, “great”, “wonderful”)，因此要将此文本映射到最终的输出$\hat{y}$
2 Prompt方法分类
3 Prompt Engineering
1 one must first consider the prompt shape,
2 then decide whether to take a manual or automated approach to create prompts of the desired shape
1 Prompt Shape
Prompt的形状主要指的是[X]和[Z]的位置和数量。
如果在句中，一般称这种prompt为cloze prompt；如果在句末，一般称这种prompt为prefix prompt。
在实际应用过程中选择哪一种主要取决于任务的形式和模型的类别。cloze prompts和Masked Language Model的训练方式非常类似，因此对于使用MLM的任务来说cloze prompts更加合适；对于生成任务来说，或者使用自回归LM解决的任务，prefix prompts就会更加合适；Full text reconstruction models较为通用，因此两种prompt均适用。另外，对于文本对的分类，prompt模板通常要给输入预留两个空，[x1]和[x2]。
2 create prompts
1 Manual Template Engineering
2 Automated Template Learning
1 Discrete Prompts
the prompt 作用在文本上
D1: Prompt Mining
D2: Prompt Paraphrasing
D3: Gradient-based Search
D4: Prompt Generation
D5: Prompt Scoring
2 Continuous Prompts
the prompt 直接作用到模型的embedding空间
C1: Prefix Tuning
C2: Tuning Initialized with Discrete Prompts
C3: Hard-Soft Prompt Hybrid Tuning
4 Answer Engineering
two dimensions that must be considered when performing answer
engineering:1 deciding the answer shape and 2 choosing an answer design method.
1 Answer Shape
和Prompt Shape啥区别？？？
2 Answer Space Design Methods
1 Manual Design
2 automatic automatic
1 Discrete Answer Search
2 Continuous Answer Search
5 Multi-Prompt Learning
之前在讨论single prompt，现在介绍multiple prompts
6 Training Strategies for Prompting Methods
1 Training Settings
full-data
few-shot /zero-shot
2 Parameter Update Methods
参考
https://arxiv.org/abs/2107.13586
刘鹏飞博士 https://zhuanlan.zhihu.com/p/395115779
https://zhuanlan.zhihu.com/p/399295895