In the context of Large Language Models (LLMs), multi-modality refers to the capability of models to process and integrate multiple types of data inputs or outputs, known as modalities, such as text, images, audio, and video. This integration allows models to develop a more comprehensive understanding of complex data, enhancing performance in tasks like visual question answering, cross-modal retrieval, text-to-image generation, and image captioning.
For instance, a multi-modal model might analyze both textual descriptions and corresponding images to generate accurate captions or answer questions about visual content. This approach leverages the strengths of each modality, leading to more robust and versatile AI applications.
Recent advancements have led to the development of large multimodal models, such as GPT-4 and Google's Gemini, which can process and generate content across various modalities, thereby broadening their applicability and effectiveness in real-world scenarios.
The LLM Knowledge Base is a collection of bite-sized explanations for commonly used terms and abbreviations related to Large Language Models and Generative AI.
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