NCA-GENL Question 131
Select 3You are assisting a senior team member in analyzing a dataset for training a generative AI language model. The dataset contains user-generated text data, but some entries have missing values and inconsistent formatting. What steps should you prioritize under the guidance of the senior team member to prepare the data effectively for further processing?
- A
Identify and fill missing values using statistical methods or default values.
- B
Remove all short text entries without checking their relevance to the task.
- C
Apply text normalization techniques such as lowercasing and removing special characters.
- D
Randomly shuffle the dataset without considering the distribution of the data.
- E
Collaborate with the senior team member to ensure the preprocessing aligns with the model requirements.
Show answer and explanation
Correct answers: A, C, E
Explanation
Data preprocessing is a crucial step in preparing a dataset for training a generative AI language model. Tasks such as handling missing values, applying text normalization, and ensuring alignment with model requirements are essential for maintaining data quality and effectiveness. Arbitrary actions, such as indiscriminately removing entries or shuffling data without consideration, can negatively impact model training and performance.
- A. Correct.
Identifying and addressing missing values ensures the dataset is complete and suitable for training, which is a key step in data preprocessing.
- B. Incorrect.
Arbitrarily removing short text entries can lead to loss of important information. The relevance of such entries should be evaluated carefully.
- C. Correct.
Text normalization is an important preprocessing step to ensure consistency in the data, which improves the model's ability to learn effectively.
- D. Incorrect.
Randomly shuffling the dataset without understanding its distribution could disrupt important relationships in the data, leading to suboptimal model performance.
- E. Correct.
Collaborating with the senior team member ensures that preprocessing aligns with the specific requirements of the model and task, which is critical for success.