Integrating multimodal content into models like Gemini presents several challenges that need to be addressed for optimal performance enhancement:
Data Integration Complexity: Multimodal data integration requires handling different types of information such as text, images, tables which may have varying structures or formats. Ensuring seamless integration without loss or distortion poses a significant challenge.
Model Architecture Complexity: Modifying existing architectures or designing new ones capable of processing multiple modalities efficiently is complex due to increased computational requirements and potential trade-offs between speed and accuracy.
Training Data Availability: Acquiring labeled multimodal datasets at scale is challenging compared to unimodal datasets due to annotation costs, data diversity issues across modalities leading to biased learning outcomes if not carefully managed.
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