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You are fine-tuning a pre-trained multimodal model for a specific task that involves generating short video clips from text prompts. The pre-trained model was trained on a large dataset of diverse videos and text descriptions. However, you observe that the fine-tuned model tends to generate video clips that are visually appealing but often deviate significantly from the meaning of the text prompts. Which of the following techniques is LEAST likely to improve the semantic consistency between the generated video clips and the text prompts?
Correct Answer: D
Freezing the weights of the video encoder will prevent it from adapting to the specific nuances of the fine-tuning task, potentially hindering the model's ability to generate videos that accurately reflect the meaning of the text prompts. A lower learning rate, reinforcement learning, data augmentation, or contrastive learning are all techniques that can help improve semantic consistency.