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.