NCA-GENM Exam Dumps | Consider a scenario where you're building a multimodal model to generate image captions. You've pre-trained

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Question 90/192

Consider a scenario where you're building a multimodal model to generate image captions. You've pre-trained a large language model (LLM) on a massive text corpus and a convolutional neural network (CNN) on ImageNet. How would you effectively combine these pre- trained components for your image captioning task, considering the need to maintain high caption quality and training efficiency?

A. Fine-tune both the CNN and the LLM jointly on the image captioning dataset.

B. Freeze the CNN, extract image features, and train the LLM to generate captions from these features.

C. Freeze the LLM, train the CNN to predict text embeddings, and then decode these embeddings into captions.

D. Use a transformer-based encoder to process both image features and text embeddings before feeding them to the LLM decoder.

E. Train the CNN and LLM separately on unrelated datasets and then combine them at inference time using a simple averaging of their outputs.

Correct Answer: A,D

Fine-tuning both the CNN and LLM jointly allows the model to adapt both visual feature extraction and language generation to the specific task of image captioning, leading to potentially higher quality captions. However, this can be computationally expensive. Using a transformer-based encoder to process both modalities before the LLM decoder allows for effective cross-modal attention and fusion, which is also a strong approach. Freezing either the CNN or LLM limits the model's ability to adapt. Training separately and averaging outputs is unlikely to produce coherent captions.

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