NCA-GENL Question 36
Single answerYou are tasked with training a generative AI language model for summarizing long documents. During the development process, you evaluate several models and use k-fold cross-validation for selecting the best-performing one. Why is k-fold cross-validation an effective technique in this scenario?
- A
It ensures the model is trained on a balanced dataset by balancing class distributions across folds.
- B
It prevents overfitting by exposing the model to multiple training and validation splits.
- C
It improves the model's performance by directly optimizing the loss function during training.
- D
It provides a robust estimation of model performance by averaging results across multiple folds.
Show answer and explanation
Correct answer: D
Explanation
K-fold cross-validation is a widely used method for model evaluation because it splits the data into k subsets and performs training and validation k times, each time using a different subset for validation and the remaining subsets for training. By averaging the results across folds, it provides a robust estimate of the model's performance on unseen data, which is essential for selecting the best-performing model in tasks like generative AI for summarization.
- A. Incorrect.
This is incorrect because k-fold cross-validation does not modify the dataset to balance class distributions. Its purpose is to evaluate the model across different splits of the dataset to estimate performance.
- B. Incorrect.
While k-fold cross-validation does involve multiple training and validation splits, its primary goal is not to prevent overfitting but to provide a reliable estimate of the model's generalization performance.
- C. Incorrect.
This is incorrect because k-fold cross-validation does not directly optimize the model's loss function. It evaluates the performance of the model over multiple folds to provide an unbiased estimate.
- D. Correct.
This is correct because k-fold cross-validation splits the dataset into k subsets (folds) and evaluates the model’s performance across these folds, providing a robust and reliable estimate of how the model is likely to perform on unseen data.