NCA-GENL Question 5
Single answerYou are training a generative AI language model using a large text dataset. To improve its ability to make accurate predictions about the next word in a sequence, you decide to use a specific algorithmic technique. Which of the following techniques is designed to handle sequential data effectively and is commonly used in generative AI models?
- A
Convolutional Neural Networks (CNNs)
- B
Recurrent Neural Networks (RNNs)
- C
Generative Adversarial Networks (GANs)
- D
Support Vector Machines (SVMs)
Show answer and explanation
Correct answer: B
Explanation
Recurrent Neural Networks (RNNs) are commonly used in generative AI tasks because they have a built-in mechanism for processing sequential data, such as text. This makes them effective in learning patterns over time, such as predicting the next word in a sentence. While other techniques like CNNs and GANs have their uses, they are not designed for sequential data processing, making RNNs the best choice for this scenario.
- A. Incorrect.
Convolutional Neural Networks (CNNs) are primarily used for tasks like image recognition and classification, not for processing sequential data like text.
- B. Correct.
Recurrent Neural Networks (RNNs) are specifically designed for handling sequential data, making them well-suited for tasks like language modeling and generative AI.
- C. Incorrect.
Generative Adversarial Networks (GANs) are used for generating data (e.g., images) but are not primarily designed for handling sequential data or predicting the next word in a sequence.
- D. Incorrect.
Support Vector Machines (SVMs) are a traditional machine learning approach and are not effective for handling the complexities of sequential data in generative AI tasks.