Large language models, such as GPT, have shown exceptional performance in text-related tasks. However, efforts are being made to teach them how to comprehend and use other forms of information, such as sounds and images. Microsoft researchers have developed DeepSpeed-VisualChat, an advanced framework that enhances multi-modal capabilities and scalability in dialogue systems. The framework uses Multi-Modal Causal Attention (MMCA) to improve the adaptability and responsiveness of multi-modal models. It achieves outstanding scalability and represents a significant step forward in multi-modal language model training.
Microsoft Researchers Propose DeepSpeed-VisualChat: A Leap Forward in Scalable Multi-Modal Language Model Training
Large language models are advanced artificial intelligence systems that can understand and produce language similar to humans on a large scale. These models have various applications, such as question-answering, content generation, and interactive dialogues. They have been trained using massive amounts of online data, which makes them highly valuable instruments for improving human-computer interaction.
Advancements in Multi-Modal Capabilities
Researchers are now working on teaching these models to comprehend and use different forms of information, including sounds and images. This advancement in multi-modal capabilities is fascinating and holds great promise. Large language models like GPT have shown exceptional performance in text-related tasks. However, to reach the level of expertise seen in human specialists and AI chatbots, these models need additional training methods like supervised fine-tuning or reinforcement learning with human guidance.
Efforts are being made to allow these models to understand and create material in various formats, including images, sounds, and videos. The DeepSpeed-VisualChat framework developed by Microsoft researchers enhances language models by incorporating multi-modal capabilities. It enables dynamic chats with multi-round and multi-picture dialogues by seamlessly fusing text and image inputs.
Scalability and Adaptability
The DeepSpeed-VisualChat framework is highly scalable, even with a language model size of 70 billion parameters. It utilizes Multi-Modal Causal Attention (MMCA), a method that estimates attention weights separately across different modalities. The framework also overcomes issues with available datasets by using data blending approaches to create a rich and varied training environment.
The architecture of DeepSpeed-VisualChat is based on MiniGPT4, where an image is encoded using a pre-trained vision encoder and aligned with the output of the text embedding layer’s hidden dimension. The framework employs the groundbreaking MMCA mechanism to improve adaptability and responsiveness.
Benefits and Future Development
DeepSpeed-VisualChat demonstrates exceptional scalability and pushes the limits of multi-modal dialogue systems. It enhances adaptation in various interaction scenarios without increasing complexity or training costs. With a language model size of 70 billion parameters, it provides a strong foundation for continued advancement in multi-modal language models.
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