NCA-GENM Question 77
Select 3You are tasked with training a multimodal generative AI model using both text and image datasets. During your initial analysis, you notice that the image dataset predominantly consists of high-resolution photos taken in bright outdoor conditions, while the text dataset contains descriptions of objects in indoor environments with low lighting. What factors should you consider to ensure the research results are not biased or skewed?
- A
The alignment between the contexts of the text and image datasets
- B
The diversity of both datasets in terms of object types and environments
- C
The resolution of the image dataset and its impact on model performance
- D
The computational resources required to process multimodal data
- E
The presence of consistent labels across both datasets
Show answer and explanation
Correct answers: A, B, E
Explanation
To ensure accurate research results, it is critical to address dataset alignment, diversity, and consistent labeling. These factors directly affect the ability of the multimodal model to identify meaningful relationships and generalize across different scenarios. Computational resources and image resolution, while important for implementation, do not directly address potential biases or trends in the datasets.
- A. Correct.
Ensuring alignment between the contexts of the text and image datasets is critical to avoid introducing bias or mismatched relationships during training.
- B. Correct.
Diversity in datasets ensures that the model can generalize well across different scenarios and reduces the risk of overfitting to specific conditions.
- C. Incorrect.
While image resolution is important for model performance, it is not directly related to addressing biases or skewed trends in research results.
- D. Incorrect.
Computational resources are essential for training but are unrelated to identifying relationships and trends in the datasets.
- E. Correct.
Consistent labels across both datasets are necessary to establish meaningful relationships between modalities and avoid errors in training.