3. Understanding Tokenization in Language Models

3. Understanding Tokenization in Language Models

University

9 Qs

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3. Understanding Tokenization in Language Models

3. Understanding Tokenization in Language Models

Assessment

Quiz

Information Technology (IT)

University

Practice Problem

Easy

Created by

Daniel K

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9 questions

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1.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

What is the definition of tokenization in NLP?

Tokenization is the process of dividing text into individual tokens, such as words or phrases.

Tokenization is the process of summarizing text into a single sentence.

Tokenization is the method of translating text into different languages.

Tokenization refers to the analysis of the sentiment of a text.

2.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

What are the main types of tokenization used in language models?

Phrase-level, sentence-level, and document-level tokenization.

Image-level, audio-level, and video-level tokenization.

Word-level, subword, and character-level tokenization.

Token-level, byte-level, and graph-level tokenization.

3.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

How does tokenization impact the performance of language models?

Tokenization only improves the speed of language models without enhancing understanding.

Tokenization enhances language model performance by improving context understanding and vocabulary management.

Tokenization reduces the complexity of language models by limiting vocabulary size.

Tokenization has no effect on the performance of language models.

4.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

What are some challenges faced during the tokenization process?

Challenges include data security, integrity maintenance, process complexity, and regulatory compliance.

Lower operational costs

Enhanced user experience

Increased transaction speed

5.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

In what ways is tokenization applied in natural language processing?

Tokenization helps in audio signal analysis.

Tokenization is applied in NLP for text segmentation, preprocessing for machine learning, and feature extraction.

Tokenization is primarily for database management systems.

Tokenization is used for image processing in computer vision.

6.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

What is the difference between word-level and subword-level tokenization?

Word-level tokenization treats each word as a token, while subword-level tokenization breaks words into smaller units.

Word-level tokenization combines multiple words into a single token.

Subword-level tokenization ignores spaces between words entirely.

Word-level tokenization uses only punctuation marks as tokens.

7.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

How does byte pair encoding (BPE) work in tokenization?

BPE splits text into individual characters without merging any pairs.

BPE encodes each byte as a unique token without considering frequency.

BPE replaces all bytes with a single byte to simplify the vocabulary.

Byte Pair Encoding (BPE) replaces the most frequent pairs of adjacent bytes with a new byte, iteratively reducing the vocabulary size.

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