Language modeling concept in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a language model in simple terms?

A language model is a tool that helps computers understand and predict the next word in a sentence, just like how we guess what someone might say next in a conversation.

beginner

Why do language models predict the next word?

Predicting the next word helps the model learn how words fit together, which is useful for tasks like writing text, answering questions, or translating languages.

intermediate

What is the difference between a unigram and a bigram model?

A unigram model looks at each word alone, while a bigram model looks at pairs of words to better guess what comes next.

intermediate

How does a neural language model differ from traditional models?

Neural language models use artificial neurons to learn complex patterns in language, making better predictions than simple counting methods like n-grams.

intermediate

What is 'perplexity' in language modeling?

Perplexity measures how well a language model predicts text; lower perplexity means the model is better at guessing the next word.

What does a language model primarily do?

A Store large amounts of text

B Predict the next word in a sentence

C Translate languages directly

D Correct grammar mistakes

Language models focus on predicting the next word to understand and generate text.

Which model considers pairs of words to predict the next word?

A Unigram model

B Neural network model

C Bigram model

D Trigram model

Bigram models look at two words together to improve prediction.

What does a lower perplexity score indicate about a language model?

A It predicts words more accurately

B It predicts words less accurately

C It uses more memory

D It runs slower

Lower perplexity means the model is better at predicting the next word.

Neural language models are better than traditional n-gram models because they:

A Only work with small datasets

B Use simple counting methods

C Ignore word order

D Learn complex patterns in language

Neural models learn complex relationships, improving predictions.

Which of these is NOT a typical use of language models?

A Image classification

B Speech recognition

C Text generation

D Machine translation

Image classification is unrelated to language modeling.

Explain what a language model is and why predicting the next word is important.

Think about how you guess what someone might say next in a conversation.

Describe the difference between traditional n-gram models and neural language models.

Consider how simple counting compares to learning complex patterns.

Practice

1. What is the main goal of a language model in natural language processing?

easy

A. To predict the next word in a sentence

B. To translate text from one language to another

C. To count the number of words in a document

D. To summarize long paragraphs into short sentences

Solution

1. Step 1: Understand the purpose of language models

   Language models are designed to understand and predict text sequences.

2. Step 2: Identify the main task of language models

   The core task is to predict the next word based on previous words in a sentence.

3. Final Answer:

   To predict the next word in a sentence -> Option A

4. Quick Check:

   Language model goal = predict next word [OK]

Hint: Language models guess the next word in text [OK]

Common Mistakes:

• Confusing language modeling with translation
• Thinking language models only count words
• Assuming summarization is the main task

2. Which of the following is the correct way to represent a bigram language model probability for a sentence "I love AI"?

easy

A. P(I) * P(love) * P(AI)

B. P(I | AI) * P(love | I) * P(AI | love)

C. P(I | love) * P(love | AI) * P(AI)

D. P(I) * P(love | I) * P(AI | love)

Solution

1. Step 1: Recall bigram model definition

   A bigram model predicts each word based on the previous word, so probabilities are conditional.

2. Step 2: Apply bigram probabilities to the sentence

   The sentence probability is P(I) * P(love | I) * P(AI | love), starting with the first word's probability.

3. Final Answer:

   P(I) * P(love | I) * P(AI | love) -> Option D

4. Quick Check:

   Bigram = word depends on previous word [OK]

Hint: Bigram means each word depends on the one before [OK]

Common Mistakes:

• Multiplying independent word probabilities (unigram)
• Using wrong conditional order
• Confusing bigram with trigram or other models

3. Given the following unigram probabilities: P(I)=0.2, P(love)=0.1, P(AI)=0.05, what is the probability of the sentence "I love AI" under a unigram model?

medium

A. 0.01

B. 0.001

C. 0.35

D. 0.0001

Solution

1. Step 1: Understand unigram model calculation

   Unigram model assumes words are independent, so multiply their probabilities.

2. Step 2: Calculate sentence probability

   Multiply P(I) * P(love) * P(AI) = 0.2 * 0.1 * 0.05 = 0.001

3. Final Answer:

   0.001 -> Option B

4. Quick Check:

   Unigram multiply all word probs = 0.001 [OK]

Hint: Multiply all word probabilities for unigram [OK]

Common Mistakes:

• Adding probabilities instead of multiplying
• Using conditional probabilities (bigram) by mistake
• Incorrect multiplication order

4. Consider this Python code snippet for a bigram model probability calculation:

sentence = ['I', 'love', 'AI']
bigram_probs = {('I', 'love'): 0.3, ('love', 'AI'): 0.4}
prob = 1.0
for i in range(len(sentence)-1):
    prob *= bigram_probs[(sentence[i], sentence[i+1])]
print(prob)

What error will occur when running this code?

medium

A. No error, prints 0.12

B. TypeError due to wrong data type in multiplication

C. KeyError because the first word probability is missing

D. IndexError because of range length

Solution

1. Step 1: Analyze the loop and dictionary access

   The loop multiplies probabilities for bigrams in the sentence using bigram_probs dictionary keys.

2. Step 2: Check if all bigrams exist in dictionary

   bigram_probs lacks a probability for the first word alone, but code only uses pairs, so no missing keys for pairs.

3. Step 3: Re-examine the code logic

   All bigrams ('I','love') and ('love','AI') exist in dictionary, so no KeyError. No TypeError or IndexError expected.

4. Final Answer:

   No error, prints 0.12 -> Option A

5. Quick Check:

   All bigrams found, multiply 0.3*0.4=0.12 [OK]

Hint: Check if all keys exist before dictionary access [OK]

Common Mistakes:

• Assuming first word needs separate probability
• Confusing KeyError with IndexError
• Ignoring dictionary key structure

5. You want to build a trigram language model to predict the next word given two previous words. Which approach best handles the problem of unseen trigrams in your training data?

hard

A. Only use unigram probabilities for all predictions

B. Ignore unseen trigrams and assign zero probability

C. Use smoothing techniques like Kneser-Ney smoothing

D. Increase the training data size without smoothing

Solution

1. Step 1: Understand the unseen trigram problem

   Unseen trigrams cause zero probabilities, which harm model predictions.

2. Step 2: Identify solution to zero probability issue

   Smoothing techniques like Kneser-Ney adjust probabilities to handle unseen cases effectively.

3. Step 3: Evaluate other options

   Ignoring unseen trigrams or only using unigram probabilities lose context; increasing data alone may not solve sparsity.

4. Final Answer:

   Use smoothing techniques like Kneser-Ney smoothing -> Option C

5. Quick Check:

   Smoothing fixes zero probs for unseen trigrams [OK]

Hint: Use smoothing to avoid zero probabilities [OK]

Common Mistakes:

• Assigning zero probability to unseen trigrams
• Ignoring context by using only unigrams
• Relying solely on more data without smoothing