想和大家分享一下最近学习的Coursera和openai联合打造ChatGPT Prompt Engineering在线课程.以下是我写的关于该课程的前两篇博客：

ChatGPT Prompt Engineering(一)
ChatGPT Prompt Engineering(二)
ChatGPT Prompt Engineering(三)

今天我们来学习第三部分内容：转换(Transforming)。

转换(Transforming)

今天我们将探索如何使用大型语言模型进行文本转换任务，例如语言翻译、拼写和语法检查、语气调整和格式转换。

首先我们需要设置通过API来访问ChatGPT的主要代码：

import openai
 
openai.api_key ='YOUR_OPENAI_API_KEY'

为防止出现Rate limits问题，我们需要安装一个为回退和重试提供函数装饰器的库 backoff

# pip install backoff
import backoff  

@backoff.on_exception(backoff.expo, openai.error.RateLimitError)
def get_completion(prompt, model="gpt-3.5-turbo", temperature=0): 
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature, 
    )
    return response.choices[0].message["content"]

翻译

ChatGPT 是使用多种语言的资源进行训练的大型语言模型。这使模型能够进行多语言翻译工作。以下是如何使用此功能的一些示例。

下面是将英语翻译成西班牙语的例子：

#将英语翻译成西班牙语

prompt = f"""
Translate the following English text to Spanish: \ 
```Hi, I would like to order a blender```
"""
response = get_completion(prompt)
print(response)

下面是让ChatGP识别文本是属于哪种语言：

prompt = f"""
Tell me which language this is: 
```Combien coûte le lampadaire?```
"""
response = get_completion(prompt)
print(response)

将文本翻译成法语、西班牙语和英语：

prompt = f"""
Translate the following  text to French and Spanish
and English pirate: \
```I want to order a basketball```
"""
response = get_completion(prompt)
print(response)

将文本翻译成正式和非正式的西班牙语：

prompt = f"""
Translate the following text to Spanish in both the \
formal and informal forms: 
'Would you like to order a pillow?'
"""
response = get_completion(prompt)
print(response)

通用翻译器

想象一下，假如您在一家大型跨国电子商务公司的IT部门负责人。客户正在用他们各自的的母语向您发送有关 IT 问题的消息。您的员工来自世界各地，且只他们只用自己的的母语。此时你就需要一个万能翻译器！

user_messages = [
  "La performance du système est plus lente que d'habitude.",  # System performance is slower than normal         
  "Mi monitor tiene píxeles que no se iluminan.",              # My monitor has pixels that are not lighting
  "Il mio mouse non funziona",                                 # My mouse is not working
  "Mój klawisz Ctrl jest zepsuty",                             # My keyboard has a broken control key
  "我的屏幕在闪烁"                                               # My screen is flashing
]

下面我们要把上述不同语言的客户问题翻译成英语和中文：

for issue in user_messages:
    prompt = f"Tell me what language this is: ```{issue}```"
    lang = get_completion(prompt)
    print(f"Original message ({lang}): {issue}")

    prompt = f"""
    Translate the following  text to English \
    and Chinese: ```{issue}```
    """
    response = get_completion(prompt)
    print(response, "\n")

语调转换

ChatGPT可以根据不同的受众输出不同语调的内容。下面是一段英语俚语的对白，我们要求ChatGPT将其转换成商业信函语调。

prompt = f"""
Translate the following from slang to a business letter: 
'Dude, This is Joe, check out this spec on this standing lamp.'
"""
response = get_completion(prompt)
print(response)

格式转换

ChatGPT 可以在不同的格式之间进行转换。提示语应描述输入和输出格式。下面我们要将一个来自于JSON格式的python字典转换为HTML格式:

data_json = { "resturant employees" :[ 
    {"name":"Shyam", "email":"shyamjaiswal@gmail.com"},
    {"name":"Bob", "email":"bob32@gmail.com"},
    {"name":"Jai", "email":"jai87@gmail.com"}
]}

prompt = f"""
Translate the following python dictionary from JSON to an HTML \
table with column headers and title: {data_json}
"""
response = get_completion(prompt)
print(response)

接下来我们要展示这个HTML页面：

from IPython.display import display, Markdown, Latex, HTML, JSON
display(HTML(response))

拼写检查/语法检查

这里我们有一些常见的语法和拼写问题的例子以及大型语言模型(LLM)的回应。

要向 LLM 发出您希望它校对您的文本的信号，您可以指示模型“校对”或“校对并更正”。下面的几段文本中存在语法错误或者拼写错误，我们要让ChatGPT来检查并纠错文本中的错误。

text = [ 
  "The girl with the black and white puppies have a ball.",  # The girl has a ball.
  "Yolanda has her notebook.", # ok
  "Its going to be a long day. Does the car need it’s oil changed?",  # Homonyms
  "Their goes my freedom. There going to bring they’re suitcases.",  # Homonyms
  "Your going to need you’re notebook.",  # Homonyms
  "That medicine effects my ability to sleep. Have you heard of the butterfly affect?", # Homonyms
  "This phrase is to cherck chatGPT for speling abilitty"  # spelling
]
for t in text:
    prompt = f"""Proofread and correct the following text
    and rewrite the corrected version. If you don't find
    and errors, just say "No errors found". Don't use 
    any punctuation around the text:
    ```{t}```"""
    response = get_completion(prompt)
    print(response)

下面我们尝试让ChatGPT来对中文的句子进行纠错，看看它是否能找出错误：

text = [ 
  "我们的公司是一家专业的电脑唯修，服务态度好，价格公道",  
  "他们在学校里学习了很多关于历史，数学和科学呢的知识。", 
  "我们去了一家新开的餐厅，才很好吃，服务员也很友好。", 
  "你得先做完作业，在出去玩！",  
  "那青山碧岭中随风起伏的阵阵林涛声，仿佛是大自然奏响的一枝交响乐.",  
  "七夕，只是牛郎与织女的节日，与俄们这些凡人无关。", 
  "我的人生有两件非常幸运的事情，一个是遇见了你，一个是将和你走过余生。"  
]
for t in text:
    prompt = f"""检查一下下面的本文是否存在错别字或者语法错误，如果有错误就重新改写句子。
    你在回复的时候必须输出2个句子，一句是原始文本，另一句是修改后的文本，如果没有发现任何错误，就说“没有找到错误”。

    请使用以下格式输出：
    原始文本：这里是原始文本
    修改后的文本：这里是修改后的文本
    
    原始文本:{t}"""
    response = get_completion(prompt)
    print(response)
    print()

总体感觉ChatGPT对中文的错别字还是比较熟悉，对一些很明显的错别字纠错成功率可以达到100%，但是对于最后一句，我们运行了多次程序后发现有时候ChatGPT对最后一句的回复是：没有找到错误。大家可以尝试一下看看ChatGPT对中文错别字的识别率到底有多少。

下面我们给ChatGPT提供一篇英语短文，让它来对短文纠错：

text = f"""
Got this for my daughter for her birthday cuz she keeps taking \
mine from my room.  Yes, adults also like pandas too.  She takes \
it everywhere with her, and it's super soft and cute.  One of the \
ears is a bit lower than the other, and I don't think that was \
designed to be asymmetrical. It's a bit small for what I paid for it \
though. I think there might be other options that are bigger for \
the same price.  It arrived a day earlier than expected, so I got \
to play with it myself before I gave it to my daughter.
"""
prompt = f"proofread and correct this review: ```{text}```"
response = get_completion(prompt)
print(response)

由于短文内容比较长，我们使用一个校对工具来看看ChatGPT到底做了哪些修改：

from redlines import Redlines

diff = Redlines(text,response)
display(Markdown(diff.output_markdown))

这里我们可以清晰的发现ChatGPT对原文做的修改。

下面我们让ChatGPT将上述原文修改为APA风格的文章(APA 风格是学术期刊文章和书籍等学术文件的写作风格和格式)

prompt = f"""
proofread and correct this review. Make it more compelling. 
Ensure it follows APA style guide and targets an advanced reader. 
Output in markdown format.
Text: ```{text}```
"""
response = get_completion(prompt)
display(Markdown(response))