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LLM Prompt Engineering - inferring 用 LLM 進行情緒辨識、主題判定

back-to:: LLM Prompt Engineering MOC

讓 LLM 判斷文字情緒​

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!!
"""

prompt = f"""
What is the sentiment of the following product review,
which is delimited with triple backticks?

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)
What is the sentiment of the following product review,
which is delimited with triple backticks?

Review text: '''{lamp_review}'''

調整 prompt : 直接 output 情緒​

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}'''
"""
response = get_completion(prompt)
print(response)
Give your answer as a single word, either "positive"
or "negative".

判斷可能還有哪些情緒​

人的情緒不只正面和負面， Ekman（1972）定義了六種基本情緒：驚訝、憤怒、厭惡、快樂、悲傷、恐懼，我們可以給定這些情緒，請 LLM 判斷某段文本含有哪幾項，或是直接請 LLM 判斷可能有哪些情緒

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!!
"""

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}'''
"""
response = get_completion(prompt)
print(response)
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.

判定是否含有特定情緒​

Is the writer of the following review expressing anger?
The review is delimited with triple backticks.
Give your answer as either yes or no.

整合: 萃取商品資訊和情緒資訊，輸出 JSON​

以往需要好幾隻模型的任務，chatGPT 可以一氣呵成

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}'''
"""
response = get_completion(prompt)
print(response)

output :

{
  "Sentiment": "positive",
  "Anger": false,
  "Item": "lamp with additional storage",
  "Brand": "Lumina"
}

判斷文章主題​

以往都要使用 LDA 這類的語言主題模型，chatGPT 也輕鬆達成

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.

我自己的嘗試 : 如果想要用更學術的分類，可以加上 `The types of topics could be based on field of study.

判斷文章是否含有特定主題​

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}'''
"""
response = get_completion(prompt)
print(response)

output :

nasa: 1
local government: 0
engineering: 0
employee satisfaction: 1
federal government: 1