Quiz: BERT & GPT Transformer Models Q&A

Are you fascinated by the world of natural language processing and the cutting-edge generative AI models that have revolutionized the way machines understand human language? Two such large language models (LLMs), BERT and GPT, stand as pillars in the field, each with unique architectures and capabilities. But how well do you know these models? In this quiz blog, we will challenge your knowledge and understanding of these two groundbreaking technologies. Before you dive into the quiz, let’s explore an overview of BERT and GPT.

Table of Contents

BERT (Bidirectional Encoder Representations from Transformers)

BERT is known for its bidirectional processing of text, allowing it to capture context from both sides of a word within a sentence. Built on the Transformer architecture, BERT utilizes only the encoder part, consisting of multiple layers of multi-head self-attention mechanisms. Pre-trained on extensive text corpora like the Toronto BookCorpus and English Wikipedia, BERT has become a versatile tool for various natural language processing tasks, from question answering to sentiment analysis.

GPT (Generative Pre-trained Transformer)

GPT, on the other hand, focuses on the unidirectional processing of text, predicting the next word in a sequence. GPT’s architecture is based on the Transformer’s decoder part, with several levels of multi-head self-attention. Pre-trained on a vast corpus like the BookCorpus, GPT has been implemented in various versions, each with increased complexity and capabilities. Its ability to generate coherent and contextually relevant text has made it a popular choice for text generation, translation, and more.

Quiz on BERT & GPT Transformer Models

#1. Which part of the Transformer architecture does BERT utilize?

Both Encoder and Decoder

Encoder

Decoder

Neither Encoder nor Decoder

#2. What is the architecture of BERT?

Only Decoder

Both Encoder and Decoder

Only Encoder

Neither Encoder nor Decoder

#3. How many unpublished books were included in the BookCorpus used for GPT-1?

6000

8000

7000

5000

#4. Which model is suitable for tasks requiring deep contextual understanding?

GPT-1

BERT

Neither BERT nor GPT-1

Both BERT and GPT-1

#5. What follows the 12-level Transformer decoder in GPT-1's architecture?

ReLU-Softmax

Linear-Softmax

Tanh-Softmax

Sigmoid-Softmax

#6. Which model uses a bidirectional approach to process text?

BERT

GPT-1

Both BERT and GPT-1

Neither BERT nor GPT-1

#7. What is the directionality of GPT-1's processing?

Left to Right

Right to Left

Bidirectional

Unidirectional

#8. What type of attention mechanism does BERT use?

Bidirectional Self-Attention

No Attention Mechanism

Unidirectional Self-Attention

Multi-Head Self-Attention

#9. What is the total number of parameters in BERTLARGE?

110 million

340 million

117 million

200 million

#10. How many attention heads are there in BERTBASE?

#11. Which model was pre-trained on the BookCorpus, including 4.5 GB of text from 7000 unpublished books?

Neither BERT nor GPT-1

BERT

Both BERT and GPT-1

GPT-1

#12. What does BERT stand for?

Bidirectional Encoder Reconstructive Transformers

Bidirectional Encoder Representations from Transformers

Bidirectional Encoder Reconfigurations from Transformers

Binary Encoder Representations from Transformers

#13. How many encoders are there in BERTBASE?

#14. What is the total number of parameters in GPT-1?

110 million

150 million

117 million

340 million

#15. What is the directionality of BERT's processing?

Unidirectional

Left to Right

Right to Left

Bidirectional

#16. What is the training objective of GPT-1?

Image classification

Masked language model

Next sentence prediction

Predicts the next word in a sequence

#17. How many encoders are there in BERTLARGE?

#18. What is the size of the BookCorpus used to pre-train GPT-1?

2.5 GB

4.5 GB

7 GB

1 GB

#19. On what dataset was BERT pre-trained?

English Wikipedia only

Toronto BookCorpus and English Wikipedia

None of the above

BookCorpus only

#20. How many attention heads are there in BERTLARGE?

#21. What type of attention mechanism does GPT-1 use?

No Attention Mechanism

Bidirectional Self-Attention

Unidirectional Self-Attention

Multi-Head Self-Attention

#22. What type of layer follows the Transformer decoder in GPT-1?

Sigmoid-Softmax

ReLU-Softmax

Linear-Softmax

Tanh-Softmax

#23. What is the training objective of BERT?

Next word prediction

Masked language model and next sentence prediction

Image classification

Text generation

#24. Which part of the Transformer architecture does GPT-1 utilize?

Encoder

Both Encoder and Decoder

Decoder

Neither Encoder nor Decoder

Finish

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

Conclusion

Whether you aced the quiz or learned something new along the way, we hope these questions has deepened your understanding of two of the most influential models in natural language processing. BERT’s bidirectional prowess and GPT’s generative capabilities continue to shape the future of AI, inspiring new innovations and applications. As the field of generative AI evolves, staying informed and engaged with these technologies is essential. Keep exploring, learning, and challenging yourself. The world of AI & generative AI in particular awaits your curiosity and creativity.

Author
Recent Posts

Ajitesh Kumar

Quiz: BERT & GPT Transformer Models Q&A

BERT (Bidirectional Encoder Representations from Transformers)

GPT (Generative Pre-trained Transformer)

Quiz on BERT & GPT Transformer Models

#1. Which part of the Transformer architecture does BERT utilize?

#2. What is the architecture of BERT?

#3. How many unpublished books were included in the BookCorpus used for GPT-1?

#4. Which model is suitable for tasks requiring deep contextual understanding?

#5. What follows the 12-level Transformer decoder in GPT-1's architecture?

#6. Which model uses a bidirectional approach to process text?

#7. What is the directionality of GPT-1's processing?

#8. What type of attention mechanism does BERT use?

#9. What is the total number of parameters in BERTLARGE?

#10. How many attention heads are there in BERTBASE?

#11. Which model was pre-trained on the BookCorpus, including 4.5 GB of text from 7000 unpublished books?

#12. What does BERT stand for?

#13. How many encoders are there in BERTBASE?

#14. What is the total number of parameters in GPT-1?

#15. What is the directionality of BERT's processing?

#16. What is the training objective of GPT-1?

#17. How many encoders are there in BERTLARGE?

#18. What is the size of the BookCorpus used to pre-train GPT-1?

#19. On what dataset was BERT pre-trained?

#20. How many attention heads are there in BERTLARGE?

#21. What type of attention mechanism does GPT-1 use?

#22. What type of layer follows the Transformer decoder in GPT-1?

#23. What is the training objective of BERT?

#24. Which part of the Transformer architecture does GPT-1 utilize?

Results

Conclusion

Ajitesh Kumar

ChatGPT Prompts (250+)

Recent Posts

Data Science / AI Trends

Free Online Tools

Newsletter

Recent Comments

Quiz: BERT & GPT Transformer Models Q&A

BERT (Bidirectional Encoder Representations from Transformers)

GPT (Generative Pre-trained Transformer)

Quiz on BERT & GPT Transformer Models

#1. Which part of the Transformer architecture does BERT utilize?

#2. What is the architecture of BERT?

#3. How many unpublished books were included in the BookCorpus used for GPT-1?

#4. Which model is suitable for tasks requiring deep contextual understanding?

#5. What follows the 12-level Transformer decoder in GPT-1's architecture?

#6. Which model uses a bidirectional approach to process text?

#7. What is the directionality of GPT-1's processing?

#8. What type of attention mechanism does BERT use?

#9. What is the total number of parameters in BERTLARGE?

#10. How many attention heads are there in BERTBASE?

#11. Which model was pre-trained on the BookCorpus, including 4.5 GB of text from 7000 unpublished books?

#12. What does BERT stand for?

#13. How many encoders are there in BERTBASE?

#14. What is the total number of parameters in GPT-1?

#15. What is the directionality of BERT's processing?

#16. What is the training objective of GPT-1?

#17. How many encoders are there in BERTLARGE?

#18. What is the size of the BookCorpus used to pre-train GPT-1?

#19. On what dataset was BERT pre-trained?

#20. How many attention heads are there in BERTLARGE?

#21. What type of attention mechanism does GPT-1 use?

#22. What type of layer follows the Transformer decoder in GPT-1?

#23. What is the training objective of BERT?

#24. Which part of the Transformer architecture does GPT-1 utilize?

Results

Conclusion

Ajitesh Kumar

ChatGPT Prompts (250+)

Recent Posts

Data Science / AI Trends

Free Online Tools

Newsletter

Tag Cloud

Recent Comments