In today’s world, where data comes in a variety of forms, from text to images and sounds, multimodal language models are becoming a key tool. What are they, and how do they work? Do they overcome the limitations of traditional text-only models? In the article, you will find everything you need to know.
What are multimodal language models?
Multimodal language models are powerful AI systems. Unlike traditional models, they can process text as well as images, videos, or sound. That’s why we call them multimodal. Trained on diverse data sets, they integrate different “modalities” of data. This allows them to synthesize information, just like in human thinking. Multimodal LLMs eliminate some of the limitations present in text-based models. They improve linguistic tasks and also cope with new ones, such as describing images or generating commands for robots. Thus, multimodality empowers LLMs to accomplish greater tasks with fewer resources.
How do they work?
Multimodal language models are based on a complex structure and the use of advanced technologies. Using neural networks, they integrate a variety of data. This generates results that take into account different aspects of information. By combining different modalities, they create a richer context. This facilitates comprehension of the content. The process of creating these models requires a variety of data sets, including text, images, videos and audio files. Multimodal models use a unified neural network architecture with different encoders for each modality. This enables the seamless integration of different types of information. If you want to make the most of those models, find yourself a Generative AI development company.
Below, you can find three examples of multimodal language models.
Some examples of multimodal LLMs
CHAT-GPT 4
GPT-4, the latest model from OpenAI, is a powerful multimodal unit capable of processing both images and text. It may not match full human performance in real-world situations. However, it achieves human-level performance in many professional and academic tests. Compared to GPT-3.5, this model demonstrates greater
- Reliability
- Creativity
- The ability to handle more complex instructions
GPT-4’s versatility allows it to manipulate both text and images. Hence, it has practical applications in various fields, such as supporting online education. Khan Academy is already planning to use GPT-4 to enhance virtual teaching. They’ll also use it to support students with varying levels of understanding.
PALM-E
PaLM-E is a groundbreaking language-visual model for robotics. By integrating data from a robot’s sensors, it enables the robot to make more complex decisions in the real world. The use of a pre-trained model combined with different modalities allows for efficient motion planning and object manipulation. But it’s not just embodied tasks. PaLM-E demonstrates knowledge transfer capability, tackling new tasks with minimal training data. It’s a step forward in robotics. It uses a wide range of information for a comprehensive approach to the robot’s interaction with the world.
KOSMOS-1
Kosmos-1 is an innovative multimodal large language model developed by Microsoft. Integrated with the transformer architecture, Kosmos-1 can efficiently process various types of sequential data, from text to images. Its ability to learn in context and adapt perception to language models allows it to solve complex tasks flexibly. The model achieves impressive accuracy in language, visual perception, and content generation tasks. Kosmos-1 represents a breakthrough in the effective use of multimodality. This allows LLMs to achieve outstanding results.
Limitations of multimodal language models
Human natural learning and integration of different sensory modalities is a complex process. Human multimodality involves the body, perceptual abilities, and the nervous system. Moreover, it develops along with the entire organism. Human language is formed based on extensive knowledge acquired from childhood.
In turn, multimodal LLMs must choose between simultaneous learning and combining components, which affects their effectiveness. Despite progress in solving the problems of language models, the risk of incompatibility with human intelligence still exists. It may manifest itself in unusual behavior.
Why do we need multimodal language models?
Multimodal language models are necessary to overcome the limitations of text-based LLMs such as GPT-3 and BERT. Human intelligence is based on various cognitive abilities, and the text, in this case, represents only a fragment of this whole. Models trained only in the text may have difficulty incorporating common sense and broad knowledge of the world. The introduction of multimodality, as in the case of GPT-4, enables more advanced natural language processing. Multimodality of language models
- Improves performance
- Enable faster decision-making
- Provide insights that may be missed by analysts or single-model systems
- Provide a richer interpretation of the data.
All these advantages are crucial in dynamic sectors such as healthcare or education.
Conclusion
In short, multimodal language models such as GPT-4, PaLM-E, and Kosmos-1 are revolutionizing AI. Their ability to process text, images, and sound allows for more advanced tasks. While there are challenges in replicating human intelligence, the benefits, such as improved efficiency and speed of decision-making, are shaping their future. Multimodality is becoming a key element, opening new perspectives in many areas.