Large Language Models (LLMs) are computer models that can analyze and generate text. They are trained on a large amount of text data to improve their performance on tasks like text generation and even coding.
Most current LLMs are text-only, meaning they excel only in text-based applications and have limited ability to understand other types of data.
Examples of plain text LLMs are GPT-3, BERT, RoBERTa, etc.
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In contrast, multimodal LLMs combine other data types such as images, video, audio, and other sensory inputs along with the text. The integration of multimodality in LLMs removes some of the limitations of current plain text models and opens possibilities for new applications that were not previously possible.
Open AI’s recently released GPT-4 is an example of multimodal LLM. It can accept image and text input and has demonstrated human-level performance across numerous benchmarks.
Rise of multimodal AI
The advancement of multimodal AI can be attributed to two crucial machine learning techniques: representation learning and transfer learning.
Representation learning allows models to develop a common representation for all modalities, while transfer learning allows them to first learn basic knowledge before refining specific domains.
These techniques are essential to make multimodal AI feasible and effective, as shown 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 are gradually becoming obsolete and the importance of understanding the connections between different modalities will continue to increase.
How multimodal LLMs work
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 numeric representation called “word embeddings”. These embeddings help the model understand the meaning and context of the text.
The Transformer model then uses attention layers to process the text and determine how different words in the input text relate to each other. This information helps the model predict the most likely next word in the output.
On the other hand, multimodal LLMs work not only with text but also with other forms of data such as images, audio and video. These models convert text and other data types into a common coding space, meaning they can handle all data types using the same mechanism. This allows the models to generate responses that include information from multiple modalities, leading to more accurate and contextual results.
Why is there a need for multimodal language models?
The plain text LLMs like GPT-3 and BERT have a wide range of applications, e.g. B. writing articles, composing emails and coding. However, this pure text approach has also revealed the limitations of these models.
Although language is a crucial part of human intelligence, it represents only one facet of our intelligence. Our cognitive abilities rely heavily on unconscious perceptions and abilities that are largely shaped by our past experiences and understanding of how the world works.
LLMs trained solely in text are inherently limited in their ability to integrate common sense and world knowledge, which can prove problematic for certain assignments. Extending the training set can help to a certain extent, but these models can still encounter unexpected knowledge gaps. Multimodal approaches can address some of these challenges.
To better understand this, consider the example of ChatGPT and GPT-4.
While ChatGPT is a remarkable language model that has proven incredibly useful in many contexts, it has certain limitations in areas like complex thinking.
To fix this, the next iteration of GPT, GPT-4, is expected to surpass ChatGPT’s reasoning capabilities. Using more advanced algorithms and incorporating multimodality, GPT-4 is poised to take natural language processing to the next level to tackle more complex reasoning problems and further enhance its ability to generate human-like responses.
GPT-4 is a large, multimodal model that can accept both image and text input and generate text output. While it may not be as capable as humans in certain real-world situations, GPT-4 has demonstrated human-level performance in numerous professional and academic benchmarks.
Compared to its predecessor GPT-3.5, the difference between the two models might be subtle in casual conversation, but becomes obvious when the complexity of a task reaches a certain threshold. GPT-4 is more reliable and creative and can handle more fine-grained instructions than GPT-3.5.
In addition, it can handle prompts with both text and images, allowing users to specify any vision or voice task. GPT-4 has demonstrated its abilities in various areas, including documents containing text, photos, charts or screenshots, and the ability to generate text outputs such as natural language and code.
Khan Academy recently announced that it will be using GPT-4 to power its AI assistant, Khanmigo, which will act as a virtual tutor for students as well as a class assistant for teachers. Each student’s ability to understand concepts varies widely, and using GPT-4 will help the organization address this issue.
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 was trained from the ground up using web data, including text and images, image-caption pairs, and text data.
The model performed impressively on speech understanding, generation, speech perception, and visual tasks. Kosmos-1 natively supports speech, perceptual language, and visual activities and can handle perceptually intensive and natural language tasks.
Kosmos-1 showed that multimodality allows large language models to do more with less and smaller models to solve complicated tasks.
PaLM-E is a new robotics model developed by researchers at Google and TU Berlin that uses knowledge transfer from different visual and linguistic domains to improve robot learning. Unlike previous efforts, PaLM-E trains the language model to directly integrate raw sensor data from the robot agent. This results in a highly effective robot learning model, a state-of-the-art general purpose visual language model.
The model accepts inputs with different types of information such as text, images and an understanding of the robot’s environment. It can produce plain text responses or a series of text 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 results show that training the model with a combination of tasks and embodiments improves its performance on each task. In addition, the model’s ability to transfer knowledge allows it to effectively solve robotic tasks even with limited training examples. This is particularly important in robotics, where obtaining adequate training data can be difficult.
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 learn language and cognition simultaneously or to combine pre-trained components. While this approach can lead to faster development and improved scalability, it can also lead to incompatibilities with human intelligence, which can manifest itself through strange or unusual behavior.
Although multimodal LLMs are making strides in addressing some critical problems of modern language models and deep learning systems, there are still limitations that need to be addressed. These limitations include potential discrepancies between the models and human intelligence, which could affect 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 their current limitations, multimodal models are poised to take over. Combining multiple data types and modalities, these models 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. In the future, however, we can expect multimodal LLMs to have an even greater impact on our lives.
The possibilities of multimodal LLMs are endless and we have only just begun to explore their true potential. Given their immense potential, it is clear that multimodal LLMs will play a crucial role in the future of AI.
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https://openai.com/research/gpt-4 https://arxiv.org/abs/2302.14045 https://www.marktechpost.com/2023/03/06/microsoft-introduces-kosmos-1-a- multimodal-large-language-model-that-can-perceive-common-modalities-follow-instructions-and-learn-in-context/ https://bdtechtalks.com/2023/03/13/multimodal-large- language-models/ https: //openai.com/customer-stories/khan-academy https://openai.com/product/gpt-4 https://jina.ai/news/paradigm-shift-towards-multimodal-ai/
I am a graduate of Civil Engineering (2022) from Jamia Millia Islamia, New Delhi and I am very interested in Data Science, especially in Neural Networks and its application in different fields.
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