NCA-GENL Question 132
Select 3You are assisting a senior team member in analyzing a dataset for training a generative AI language model. The dataset contains text samples from multiple sources, but the senior team member has instructed you to ensure it meets the required quality standards before proceeding. Which steps should you prioritize during the data analysis process?
- A
Identify and remove duplicate text entries within the dataset.
- B
Normalize text formatting, such as converting all text to lowercase and removing special characters.
- C
Analyze the dataset for potential biases or imbalances in the sources.
- D
Ensure the dataset is as large as possible, even if some sources are low quality.
- E
Validate the dataset against the model’s intended application and target audience.
Show answer and explanation
Correct answers: A, C, E
Explanation
When conducting data analysis under supervision, it is important to focus on steps that ensure the dataset is clean, unbiased, and aligned with the model’s purpose. Removing duplicates, checking for bias, and ensuring the dataset fits the target application are all key priorities. However, steps like normalizing text formatting or simply increasing dataset size without considering quality are secondary and context-dependent.
- A. Correct.
Duplicate text entries can introduce redundancy and reduce the effectiveness of training, so identifying and removing them is a critical step in data analysis.
- B. Incorrect.
While normalizing text formatting (e.g., converting to lowercase) can be useful in certain NLP tasks, this step is not universally required and depends on the specific use case of the model.
- C. Correct.
Analyzing for biases or imbalances is essential to ensure the model learns from a fair and representative dataset, reducing the risk of biased outputs.
- D. Incorrect.
Increasing the size of the dataset is beneficial only if the data quality is maintained. Including low-quality sources can degrade model performance.
- E. Correct.
Validating the dataset against the model's intended application ensures the data aligns with the goals and audience, which is crucial for achieving relevant and accurate results.