Speculations about the impending launch of Generative Pre-trained Transformer 4 or GPT-4 and its subsequent official announcement and confirmation on 14 March 2023 have created a wider public interest in multimodal language models and multimodal AI modeling. The artificial intelligence research lab OpenAI described its newer GPT language model as more advanced than the previous version of GPT such as GPT-3 and GPT-3.5 due to the multimodal language modeling that underscores its functionalities and capabilities.
Language models are the foundation of natural language processing or NLP and they power several artificial intelligence applications such as chatbots and generative AI products or services. The quality of a particular model determines the capabilities of a particular application. Large language models power advanced NLP applications and the more specific multimodal language models are behind modern and next-generation NLP applications. What exactly are multimodal language models? What are their advantages and disadvantages?
Explaining Multimodal Language Models
A traditional language model is trained using datasets made of texts. Other large language models are also trained using larger textual data. A multimodal language model is trained using different modalities of data such as written text and spoken language, visual data such as images and videos, numeric data, and sensor data, among others.
The concept and its application in AI modeling draw inspiration from multimodal learning and the multimodal nature of textbook materials. Take note that multimodal learning involves using different modes of learning such as visual, auditory, and kinesthetic learning to provide a well-rounded learning experience. Textbooks are multimodal in nature because they impart information to their readers through texts and images.
Multimodal language models fundamentally can learn and understand or process language better using more than one way of getting information. This is similar to an average person who learns and understands from using different learning modalities or to an individual who can learn better using different modes of communication and learning styles.
It is also important to note that a particular multimodal language model can use different data modalities as input to generate desired outputs. Nevertheless, considering this main characteristic, it can be described that multimodal language models are specific NLP models trained using verbal and non-verbal data via deep learning to enable them to understand verbal and non-verbal inputs and generate verbal output.
Several language models have demonstrated the capabilities of multimodal LLMs. GPT-4 has better natural language processing capabilities and it also has some computer vision capabilities via image recognition. It can recognize images such as photographs, graphs, tables, and infographics and generate image descriptions.
Advantages of Multimodal Language Models
Multimodal language models have several advantages over text-only traditional language models. They have more expansive applications and they can produce more accurate results or outcomes that are closer to desired results. Understanding the capabilities of multimodal models requires a further look at their specific advantages.
Below are the notable advantages or benefits:
• Better Understanding of Context: These models can have a more accurate understanding of the context in which the language is being used because it can incorporate different modalities of data.
• Improved Downstream Tasks: Another advantage of multimodal language models is that they outperform traditional models on different downstream tasks such as image captioning, visual question-answering, and natural language inference.
• More Robust to Data Nuisances: They also tend to be more robust to instances in which data are noisy or incomplete than text-only traditional language models because of the fact that they can recognize non-text data.
• Verbal and Non-Verbal Input: Multimodal language models expand computer-user interactions. A particular AI application can receive input based on different data including textual data, visual data, and sensor data, among others.
• Expanded Generative Applications: A particular generative AI app using a multimodal LLM can generate outputs that incorporate different data modalities. An example would be generating an image complete with copies or an infographic.
Disadvantages of Multimodal Language Models
Developing and implementing a multimodal language model can be resource-intensive because of the amount of data and different data modalities that are needed. It can also be more expensive than traditional models due to its complexities. It is important to understand the drawbacks of multimodal language models to understand further their capabilities.
Below are the notable disadvantages or drawbacks:
• They Are More Complicated: A particular multimodal model is inherently complex. It is trained using large amounts of data and different data modalities. This creates unique training and deployment issues and challenges.
• Higher Data Requirement: It is also important to highlight the fact that a particular model would require large amounts of diverse data for it to be trained effectively. Collecting and labeling these data are expensive and time-consuming.
• Integrating Data Modalities: Another disadvantage of multimodal language models is that their development presents challenges in integrating different modalities of data. Different modalities can have different levels of noise and bias.
• Domain-Specific Limitations: These models can be restricted to a particular domain or use a case in which they were trained on and are intended to be applied or deployed. This means that a model is not universal in terms of applications.