NCA-GENM Question 74
Select 3A team of researchers is using a generative multimodal AI model to analyze medical imaging data and generate corresponding textual diagnoses. They observe inconsistencies in the generated diagnoses for patients with similar imaging data. Which factors should they investigate to identify relationships or trends affecting the results?
- A
Biases present in the training dataset used for the model
- B
The performance of the underlying hardware during model inference
- C
The diversity and quality of the input imaging data
- D
The model's ability to align textual and visual modalities effectively
- E
The choice of optimizer used during model training
Show answer and explanation
Correct answers: A, C, D
Explanation
To identify relationships or trends affecting the results in a generative multimodal AI model, it is essential to examine the training data for biases, ensure input data quality and diversity, and evaluate how effectively the model aligns its multimodal components. These factors directly influence the model's ability to generate consistent and reliable outputs. Factors like hardware performance or specific optimizers play a more peripheral role in this context.
- A. Correct.
Biases in the training dataset can impact the model's ability to generate consistent and accurate results, as the model may have learned skewed patterns that affect its performance.
- B. Incorrect.
While hardware performance during inference can influence processing speed, it is unlikely to directly cause inconsistencies in the model's outputs unless there are hardware-specific errors.
- C. Correct.
The diversity and quality of the input imaging data are critical for the model to generalize effectively and identify consistent relationships or trends across input samples.
- D. Correct.
The model's ability to align textual and visual modalities is key in ensuring consistency between the input data (e.g., imaging) and the generated textual outputs.
- E. Incorrect.
The choice of optimizer primarily impacts the training process but is less likely to directly cause inconsistencies during inference unless the training itself was flawed.