Perplexity for research and fact-checking in AI for Everyone - Mini Project: Build & Apply

Understanding Perplexity for Research and Fact-Checking

📖 Scenario: You are a researcher who wants to understand how to measure the quality of language models when they help with research and fact-checking.

🎯 Goal: Build a simple explanation and example of how perplexity works and why it matters for checking facts and research accuracy.

📋 What You'll Learn

Create a dictionary called sentence_probabilities with exact word probabilities

Add a variable called total_words with the exact number of words

Calculate the perplexity using the formula with a for loop over sentence_probabilities

Add a final variable called perplexity that holds the calculated value

💡 Why This Matters

🌍 Real World

Perplexity helps researchers understand how well a language model predicts text, which is important for fact-checking and generating accurate information.

💼 Career

Data scientists and AI researchers use perplexity to evaluate and improve language models used in search engines, chatbots, and automated fact-checking tools.

1

Create word probabilities dictionary

Create a dictionary called sentence_probabilities with these exact entries: 'the': 0.1, 'cat': 0.05, 'sat': 0.02, 'on': 0.03, 'mat': 0.01.

AI for Everyone

# Create the dictionary sentence_probabilities with given word probabilities
# Your code here

Hint

Use curly braces to create a dictionary with the exact word-probability pairs.

2

Add total words variable

Add a variable called total_words and set it to the exact number 5, representing the total words in the sentence.

AI for Everyone

sentence_probabilities = {'the': 0.1, 'cat': 0.05, 'sat': 0.02, 'on': 0.03, 'mat': 0.01}
# Add the total_words variable below
# Your code here

Hint

Count the words in the dictionary and assign that number to total_words.

3

Calculate perplexity using loop

Create a variable called product_prob and set it to 1. Then use a for loop with variables word and prob to iterate over sentence_probabilities.items(). Inside the loop, multiply product_prob by prob.

AI for Everyone

sentence_probabilities = {'the': 0.1, 'cat': 0.05, 'sat': 0.02, 'on': 0.03, 'mat': 0.01}
total_words = 5
# Calculate product_prob by multiplying all probabilities
# Your code here

Hint

Start with 1 and multiply by each probability in the dictionary using a loop.

4

Compute final perplexity value

Add a variable called perplexity and set it to product_prob raised to the power of -1 / total_words.

AI for Everyone

sentence_probabilities = {'the': 0.1, 'cat': 0.05, 'sat': 0.02, 'on': 0.03, 'mat': 0.01}
total_words = 5
product_prob = 1
for word, prob in sentence_probabilities.items():
    product_prob *= prob
# Calculate perplexity below
# Your code here

Hint

Use the formula: perplexity = product_prob to the power of (-1 divided by total_words).