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Large language models (LLMs) are computer models capable of analyzing and generating text. They are trained on a vast amount of textual data to enhance their performance in tasks like text generation and even coding.
Most current LLMs are text-only, i.e., they excel only at text-based applications and have limited ability to understand other types of data.
Examples of text-only LLMs include GPT-3, BERT, RoBERTa, etc.
On the contrary, Multimodal LLMs combine other data types, such as images, videos, audio, and other sensory inputs, along with the text. The integration of multimodality into LLMs addresses some of the limitations of current text-only models and opens up possibilities for new applications that were previously impossible.
The recently released GPT-4 by Open AI is an example of Multimodal LLM. It can accept image and text inputs and has shown human-level performance on numerous benchmarks.
Rise in Multimodal AI
The advancement of multimodal AI can be credited to two crucial machine learning techniques: Representation learning and transfer learning.
With representation learning, models can develop a shared representation for all modalities, while transfer learning allows them to first learn fundamental knowledge before fine-tuning on specific domains.
These techniques are essential for making multimodal AI feasible and effective, as seen by recent breakthroughs such as CLIP, which aligns images and text, and DALL·E 2 and Stable Diffusion, which generate high-quality images from text prompts.
As the boundaries between different data modalities become less clear, we can expect more AI applications to leverage relationships between multiple modalities, marking a paradigm shift in the field. Ad-hoc approaches will gradually become obsolete, and the importance of understanding the connections between various modalities will only continue to grow.
Working of Multimodal LLMs
Text-only Language Models (LLMs) are powered by the transformer model, which helps them understand and generate language. This model takes input text and converts it into a numerical representation called “word embeddings.” These embeddings help the model understand the meaning and context of the text.
The transformer model then uses something called “attention layers” to process the text and determine how different words in the input text are related to each other. This information helps the model predict the most likely next word in the output.
On the other hand, Multimodal LLMs work with not only text but also other forms of data, such as images, audio, and video. These models convert text and other data types into a common encoding space, which means they can process all types of data using the same mechanism. This allows the models to generate responses incorporating information from multiple modalities, leading to more accurate and contextual outputs.
Why is there a need for Multimodal Language Models
The text-only LLMs like GPT-3 and BERT have a wide range of applications, such as writing articles, composing emails, and coding. However, this text-only approach has also highlighted the limitations of these models.
Although language is a crucial part of human intelligence, it only represents one facet of our intelligence. Our cognitive capacities heavily rely on unconscious perception and abilities, largely shaped by our past experiences and understanding of how the world operates.
LLMs trained solely on text are inherently limited in their ability to incorporate common sense and world knowledge, which can prove problematic for certain tasks. Expanding the training data set can help to some degree, but these models may still encounter unexpected gaps in their knowledge. Multimodal approaches can address some of these challenges.
To better understand this, consider the example of ChatGPT and GPT-4.
Although ChatGPT is a remarkable language model that has proven incredibly useful in many contexts, it has certain limitations in areas like complex reasoning.
To address this, the next iteration of GPT, GPT-4, is expected to surpass ChatGPT’s reasoning capabilities. By using more advanced algorithms and incorporating multimodality, GPT-4 is poised to take natural language processing to the next level, allowing it to tackle more complex reasoning problems and further improve its ability to generate human-like responses.
GPT-4 is a large, multimodal model that can accept both image and text inputs and generate text outputs. Although it may not be as capable as humans in certain real-world situations, GPT-4 has shown human-level performance on numerous professional and academic benchmarks.
Compared to its predecessor, GPT-3.5, the distinction between the two models may be subtle in casual conversation but becomes apparent when the complexity of a task reaches a certain threshold. GPT-4 is more reliable and creative and can handle more nuanced instructions than GPT-3.5.
Moreover, it can handle prompts involving text and images, which allows users to specify any vision or language task. GPT-4 has demonstrated its capabilities in various domains, including documents that contain text, photographs, diagrams, or screenshots, and can generate text outputs such as natural language and code.
Khan Academy has recently announced that it will use GPT-4 to power its AI assistant Khanmigo, which will act as a virtual tutor for students as well as a classroom assistant for teachers. Each student’s capability to grasp concepts varies significantly, and the use of GPT-4 will help the organization tackle this problem.
Kosmos-1 is a Multimodal Large Language Model (MLLM) that can perceive different modalities, learn in context (few-shot), and follow instructions (zero-shot). Kosmos-1 has been trained from scratch on web data, including text and images, image-caption pairs, and text data.
The model achieved impressive performance on language understanding, generation, perception-language, and vision tasks. Kosmos-1 natively supports language, perception-language, and vision activities, and it can handle perception-intensive and natural language tasks.
Kosmos-1 has demonstrated that multimodality allows large language models to achieve more with less and enables smaller models to solve complicated tasks.
PaLM-E is a new robotics model developed by researchers at Google and TU Berlin that utilizes knowledge transfer from various visual and language domains to enhance robot learning. Unlike prior efforts, PaLM-E trains the language model to incorporate raw sensor data from the robotic agent directly. This results in a highly effective robot learning model, a state-of-the-art general-purpose visual-language model.
The model takes in inputs with different information types, such as text, pictures, and an understanding of the robot’s surroundings. It can produce responses in plain text form or a series of textual instructions that can be translated into executable commands for a robot based on a range of input information types, including text, images, and environmental data.
PaLM-E demonstrates competence in both embodied and non-embodied tasks, as evidenced by the experiments conducted by the researchers. Their findings indicate that training the model on a combination of tasks and embodiments enhances its performance on each task. Additionally, the model’s ability to transfer knowledge enables it to solve robotic tasks even with limited training examples effectively. This is especially important in robotics, where obtaining adequate training data can be challenging.
Limitations of Multimodal LLMs
Humans naturally learn and combine different modalities and ways of understanding the world around them. On the other hand, Multimodal LLMs attempt to simultaneously learn language and perception or combine pre-trained components. While this approach can lead to faster development and improved scalability, it can also result in incompatibilities with human intelligence, which may be exhibited through strange or unusual behavior.
Although multimodal LLMs are making headway in addressing some critical issues of modern language models and deep learning systems, there are still limitations to be addressed. These limitations include potential mismatches between the models and human intelligence, which could impede their ability to bridge the gap between AI and human cognition.
Conclusion: Why are Multimodal LLMs the future?
We are currently at the forefront of a new era in artificial intelligence, and despite its current limitations, multimodal models are poised to take over. These models combine multiple data types and modalities and have the potential to completely transform the way we interact with machines.
Multimodal LLMs have achieved remarkable success in computer vision and natural language processing. However, in the future, we can expect multimodal LLMs to have an even more significant impact on our lives.
The possibilities of multimodal LLMs are endless, and we have only begun to explore their true potential. Given their immense promise, it’s clear that multimodal LLMs will play a crucial role in the future of AI.
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I am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.