Vision Language Models When AI Learns to See

The provided source introduces Vision Language Models (VLMs), explaining how they overcome the limitations of traditional Large Language Models (LLMs) by enabling the processing of both text and images. VLMs are multimodal, allowing them to interpret visual data such as photographs, graphs, and handwritten notes, converting them into a format that LLMs can understand. The process involves a vision encoder transforming images into feature vectors, which are then mapped into image tokens by a projector, allowing them to be processed alongside text tokens by the LLM's attention mechanisms. The text also highlights challenges with VLMs, including tokenization bottlenecks, the potential for hallucinations due to statistical associations rather than human-like understanding, and biases inherited from training data. Ultimately, VLMs extend LLM capabilities, allowing AI to not just read, but also to "see," interpret, and reason about the visual world.