The emergence of Multimodality Large Language Models (MLLMs) like GPT-4 and Gemini has spurred interest in combining language understanding with vision. While models like BLIP and LLaMA-Adapter show promise, they need more training data. Researchers have developed SPHINX-X, which significantly advances MLLMs, demonstrating superior performance and generalization while offering a platform for multi-modal instruction tuning.
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The Emergence of Multimodality Large Language Models (MLLMs)
The emergence of Multimodality Large Language Models (MLLMs), such as GPT-4 and Gemini, has sparked significant interest in combining language understanding with various modalities like vision. This fusion offers potential for diverse applications, from embodied intelligence to GUI agents.
Challenges and Solutions
Despite the rapid development of open-source MLLMs like BLIP and LLaMA-Adapter, their performance could be improved by more training data and model parameters. Researchers have developed SPHINX-X, an advanced MLLM series built upon the SPHINX framework, which addresses these challenges.
Recent Advancements in LLMs
Recent advancements in LLMs have leveraged Transformer architectures, with innovations like Mistral’s window attention and Mixtral’s sparse MoE layers. MLLMs integrate non-text encoders for visual understanding, pushing the boundaries of vision-language fusion.
SPHINX-X MLLMs
The SPHINX-X MLLMs demonstrate state-of-the-art performance across various multi-modal tasks, showcasing capabilities in language hallucination, visual illusion, aesthetic perception, GUI element localization, and visual understanding.
Conclusion
SPHINX-X significantly advances MLLMs, building upon the SPHINX framework. Through enhancements in architecture, training efficiency, and dataset enrichment, SPHINX-X exhibits superior performance and generalization compared to the original model.
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