41. Which of the following is an example of a task that GPT models can be fine-tuned for?
A) Image classification
B) Sentiment analysis
C) Speech recognition
D) Object detection
42. What is the “in-context learning” feature of GPT-3?
A) The ability to generate text in multiple languages
B) The ability to learn from a small amount of task-specific input during generation
C) The ability to control the length and complexity of generated text
D) The ability to generate text based on specific prompts
43. Which of the following is a limitation of GPT models for real-world applications?
A) They are not capable of generating text that is indistinguishable from human-generated text
B) They require large amounts of training data
C) They are computationally expensive to train and run
D) They have a limited understanding of common sense knowledge
44. Which of the following is an example of a domain-specific application of GPT models?
A) Generating captions for images
B) Generating product reviews
C) Generating news articles
D) Generating scientific research papers
45. Which of the following techniques is used in GPT models to prevent overfitting during training?
A) Dropout
B) Batch normalization
C) Weight decay
D) All of the above
