DeepLearning.AIとOpenAIが公開したChatGPT Prompt Engineering（4-推測）
はじめに
ChatGPT Prompt Engineeringを受講したメモです。
全体の目次はこちらをご覧ください。
今回は、推測（Inferring）です。
長い文章から評価や、トピックを推測します。
特定のトピックをアラートするのは面白いと思いました。
製品レビューの評価
こちらのレビューから情報を推測してみます。
レビュー
lamp_review = """
Needed a nice lamp for my bedroom, and this one had
additional storage and not too high of a price point.
Got it fast. The string to our lamp broke during the
transit and the company happily sent over a new one.
Came within a few days as well. It was easy to put
together. I had a missing part, so I contacted their
support and they very quickly got me the missing piece!
Lumina seems to me to be a great company that cares
about their customers and products!!
"""
Positive/Negative
評価が肯定的か否定的か
prompt = f"""
What is the sentiment of the following product review,
which is delimited with triple backticks?
Give your answer as a single word, either "positive" \
or "negative".
Review text: '''{lamp_review}'''
"""
結果
Positive
感情分類
prompt = f"""
Identify a list of emotions that the writer of the \
following review is expressing. Include no more than \
five items in the list. Format your answer as a list of \
lower-case words separated by commas.
Review text: '''{lamp_review}'''
"""
結果
happy, satisfied, grateful, impressed, content
どの製品、会社に対するレビューか（Json出力）
prompt = f"""
Identify the following items from the review text:
- Item purchased by reviewer
- Company that made the item
The review is delimited with triple backticks. \
Format your response as a JSON object with \
"Item" and "Brand" as the keys.
If the information isn't present, use "unknown" \
as the value.
Make your response as short as possible.
Review text: '''{lamp_review}'''
"""
結果
{
"Item": "lamp",
"Brand": "Lumina"
}
上の例をまとめて出力
prompt = f"""
Identify the following items from the review text:
- Sentiment (positive or negative)
- Is the reviewer expressing anger? (true or false)
- Item purchased by reviewer
- Company that made the item
The review is delimited with triple backticks. \
Format your response as a JSON object with \
"Sentiment", "Anger", "Item" and "Brand" as the keys.
If the information isn't present, use "unknown" \
as the value.
Make your response as short as possible.
Format the Anger value as a boolean.
Review text: '''{lamp_review}'''
"""
結果
{
"Sentiment": "positive",
"Anger": false,
"Item": "lamp with additional storage",
"Brand": "Lumina"
}
ニュース推測
次は、下のニュースを推測してみます。
::details ニュース文
story = """
In a recent survey conducted by the government,
public sector employees were asked to rate their level
of satisfaction with the department they work at.
The results revealed that NASA was the most popular
department with a satisfaction rating of 95%.
One NASA employee, John Smith, commented on the findings,
stating, "I'm not surprised that NASA came out on top.
It's a great place to work with amazing people and
incredible opportunities. I'm proud to be a part of
such an innovative organization."
The results were also welcomed by NASA's management team,
with Director Tom Johnson stating, "We are thrilled to
hear that our employees are satisfied with their work at NASA.
We have a talented and dedicated team who work tirelessly
to achieve our goals, and it's fantastic to see that their
hard work is paying off."
The survey also revealed that the
Social Security Administration had the lowest satisfaction
rating, with only 45% of employees indicating they were
satisfied with their job. The government has pledged to
address the concerns raised by employees in the survey and
work towards improving job satisfaction across all departments.
"""
:::
5つトピックを推測
prompt = f"""
Determine five topics that are being discussed in the \
following text, which is delimited by triple backticks.
Make each item one or two words long.
Format your response as a list of items separated by commas.
Text sample: '''{story}'''
"""
結果
government survey, job satisfaction, NASA, Social Security Administration, employee concerns
特定のトピックが含まれているかどうか
topic_list = [
"nasa", "local government", "engineering",
"employee satisfaction", "federal government"
]
prompt = f"""
Determine whether each item in the following list of \
topics is a topic in the text below, which
is delimited with triple backticks.
Give your answer as list with 0 or 1 for each topic.\
List of topics: {", ".join(topic_list)}
Text sample: '''{story}'''
"""
結果
nasa: 1
local government: 0
engineering: 0
employee satisfaction: 1
federal government: 1
特定のトピックが含まれていたらアラート
topic_dict = {i.split(': ')[0]: int(i.split(': ')[1]) for i in response.split(sep='\n')}
if topic_dict['nasa'] == 1:
print("ALERT: New NASA story!")
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