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. How many encoders are there in BERTLARGE?

#2. What is the architecture of BERT?

Only Decoder

Both Encoder and Decoder

Neither Encoder nor Decoder

Only Encoder

#3. What does BERT stand for?

Binary Encoder Representations from Transformers

Bidirectional Encoder Reconstructive Transformers

Bidirectional Encoder Reconfigurations from Transformers

Bidirectional Encoder Representations from Transformers

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

Sigmoid-Softmax

ReLU-Softmax

Linear-Softmax

Tanh-Softmax

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

110 million

340 million

117 million

200 million

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

4.5 GB

7 GB

1 GB

2.5 GB

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

Sigmoid-Softmax

Linear-Softmax

Tanh-Softmax

ReLU-Softmax

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

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

Neither BERT nor GPT-1

GPT-1

BERT

Both BERT and GPT-1

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

Both BERT and GPT-1

Neither BERT nor GPT-1

GPT-1

BERT

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 Which model was pre-trained on the BookCorpus, including 4.5 GB of text from 7000 unpublished books?

Neither BERT nor GPT-1

GPT-1

BERT

Both BERT and GPT-1

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

Image classification

Next sentence prediction

Masked language model

Predicts the next word in a sequence

#13. What is the training objective of BERT?

Masked language model and next sentence prediction

Text generation

Image classification

Next word prediction

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

BookCorpus only

Toronto BookCorpus and English Wikipedia

English Wikipedia only

None of the above

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

150 million

117 million

110 million

340 million

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

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

No Attention Mechanism

Multi-Head Self-Attention

Bidirectional Self-Attention

Unidirectional Self-Attention

#18. How many encoders are there in BERTBASE?

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

Encoder

Decoder

Neither Encoder nor Decoder

Both Encoder and Decoder

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

Left to Right

Unidirectional

Bidirectional

Right to Left

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

Right to Left

Left to Right

Bidirectional

Unidirectional

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

Decoder

Both Encoder and Decoder

Encoder

Neither Encoder nor Decoder

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

7000

8000

6000

5000

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

Unidirectional Self-Attention

Multi-Head Self-Attention

Bidirectional Self-Attention

No Attention Mechanism

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.
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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.

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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. How many encoders are there in BERTLARGE?

#2. What is the architecture of BERT?

#3. What does BERT stand for?

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

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

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

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

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

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

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

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

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

#13. What is the training objective of BERT?

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

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

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

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

#18. How many encoders are there in BERTBASE?

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

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

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

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

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

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

Results

Conclusion

Ajitesh Kumar

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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. How many encoders are there in BERTLARGE?

#2. What is the architecture of BERT?

#3. What does BERT stand for?

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

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

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

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

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

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

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

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

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

#13. What is the training objective of BERT?

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

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

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

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

#18. How many encoders are there in BERTBASE?

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

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

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

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

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

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

Results

Conclusion

Ajitesh Kumar

ChatGPT Prompts (250+)

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Data Science / AI Trends

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