NCA-GENM Question 33
Select 3You are tasked with fine-tuning a multimodal generative AI model for a specific application that involves generating product descriptions based on both text and image inputs. Which of the following steps best align with preparing and developing content for multimodal-specific transfer learning?
- A
Curate a dataset that contains paired text and image data relevant to the target domain.
- B
Train a new multimodal model from scratch using random initialization.
- C
Ensure the dataset is preprocessed to align image and text modalities, such as resizing images and tokenizing text.
- D
Utilize a pre-trained multimodal foundation model and fine-tune it with a domain-specific dataset.
- E
Focus exclusively on text data since image inputs are secondary in multimodal scenarios.
Show answer and explanation
Correct answers: A, C, D
Explanation
Multimodal-specific transfer learning involves leveraging pre-trained models and domain-specific datasets to adapt the model for a specific application. Key steps include curating paired multimodal data, preprocessing it to align modalities, and fine-tuning a pre-trained multimodal model. Training from scratch or excluding one of the modalities would not align with effective transfer learning practices.
- A. Correct.
Curating a dataset with paired text and image data is a critical step for multimodal transfer learning, as it ensures the model can learn the relationship between the two modalities for the specific domain.
- B. Incorrect.
Training a model from scratch is generally not recommended for transfer learning, especially in multimodal tasks, due to the high computational cost and lack of leverage from pre-trained models.
- C. Correct.
Preprocessing the dataset to align image and text modalities is essential for multimodal tasks, as the model needs these inputs in a compatible format for effective learning.
- D. Correct.
Using a pre-trained multimodal foundation model for fine-tuning is a best practice in transfer learning, as it builds upon the foundational knowledge already present in the model, reducing training time and improving performance.
- E. Incorrect.
Focusing exclusively on text data neglects the multimodal nature of the task and would fail to utilize the image data, which is critical for generating accurate product descriptions in this scenario.