Recent developments in Multi-Modal (MM) pre-training have led to the creation of sophisticated MM-LLMs (MultiModal Large Language Models) by integrating Large Language Models (LLMs) with additional modalities. Models like GPT-4(Vision) and Gemini demonstrate remarkable capabilities in processing multimodal content. Research has focused on aligning and tuning various modalities in MM-LLMs to enhance their capabilities. Read more at https://arxiv.org/abs/2401.13601.
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Recent Developments in Multi-Modal Pre-Training
Recent advancements in Multi-Modal (MM) pre-training have significantly improved the capabilities of Machine Learning (ML) models to process and understand various data types, including text, images, audio, and video.
Enhanced Models
The integration of Large Language Models (LLMs) with multimodal data processing has resulted in sophisticated MultiModal Large Language Models (MM-LLMs). These models, such as GPT-4(Vision) and Gemini, have demonstrated exceptional abilities in comprehending and producing multimodal content.
Advantages of MM-LLMs
MM-LLMs leverage pre-trained unimodal models, such as LLMs, and incorporate additional modalities to reduce computing costs while enhancing the model’s ability to handle diverse data types. This approach also focuses on increasing the capabilities of conventional LLMs while allowing them to perform well across a wider range of multimodal tasks.
Research Insights
Researchers have been working on aligning and tuning various modalities to function in line with human intents and comprehension. The study conducted by a team of researchers from Tencent AI Lab, Kyoto University, and Shenyang Institute of Automation offers valuable insights into the design and evaluation of MM-LLMs, providing a comprehensive overview of their architecture, composition, and performance against industry standards.
Key Components of MM-LLMs
The study explores the five key components of the general model architecture of MM-LLMs, including Modality Encoder, LLM Backbone, Modality Generator, Input Projector, and Output Projector.
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