One of the most exciting and interesting developments in the rapidly changing domain of machine learning and artificial intelligence is the arrival of a multimodal Chain of Thought (CoT) prompting. This new technique can completely change how AI systems understand, process, and generate results through combining and analyzing different kinds of data.
What is Multimodal CoT Prompting?
Multimodal CoT prompting combines the ideas of Chain-of-Thought (CoT) prompting with multimodal inputs, which allows AI systems to execute advanced reasoning across a variety of data, such as text, pictures, audio and video.
How does Multimodal CoT Prompting work?
Multimodal CoT Prompting consists of multiple steps: first, data from different modes is collected and processed, then models like BERT for text, ResNET for images, and wav2Vec for audio are used to transform the data into high-dimensional embeddings. After that, these embeddings are combined into a single representation by applying techniques like attention mechanisms or concatenation. Step-by-step reasoning is applied to the integrated data, which produces intermediate results that are then expanded to produce the final output.
Block Diagram