NCA-GENL Question 1
Select 3A team is training a generative AI model using a large dataset of customer interactions to predict the next sentence in a conversation. They are debating between using a standard Recurrent Neural Network (RNN) or a Transformer-based architecture. Which of the following reasons support choosing a Transformer-based architecture?
- A
Transformers handle long-range dependencies better compared to RNNs.
- B
Transformers process input sequences in parallel, leading to faster training times.
- C
RNNs are more effective for capturing complex relationships in long texts.
- D
Transformers use attention mechanisms, which allow the model to focus on relevant parts of the input sequence.
- E
Transformers are less memory-intensive than RNNs during training.
Show answer and explanation
Correct answers: A, B, D
Explanation
Transformers have become the preferred architecture for many generative AI tasks, including text prediction, due to their ability to handle long-range dependencies, efficient parallel processing, and the use of attention mechanisms to focus on relevant input. These advantages address the limitations of RNNs, particularly for large datasets and complex tasks.
- A. Correct.
Transformers are designed to handle long-range dependencies effectively using attention mechanisms, which is a key limitation of RNNs.
- B. Correct.
Transformers process sequences in parallel rather than sequentially (as RNNs do), enabling faster training times on large datasets.
- C. Incorrect.
While RNNs can capture dependencies in sequential data, they struggle with long-range dependencies due to issues like vanishing gradients.
- D. Correct.
Attention mechanisms in Transformers allow the model to focus on the most relevant parts of the input, improving performance on tasks like text prediction.
- E. Incorrect.
Transformers are generally more memory-intensive than RNNs due to the computational cost of attention mechanisms and parallel processing.