Large language models (LLMs) handle visual information by first converting images into a format compatible with their text-based architecture. While LLMs primarily process text tokens, they can incorporate visual data by using separate encoder models to transform images into embeddings—numerical representations similar to text tokens. For example, an image might be processed by a vision encoder like CLIP or a Vision Transformer (ViT), which breaks the image into patches and converts them into a sequence of vectors. These vectors are then treated as additional tokens in the model’s context window, allowing the LLM to process them alongside text tokens during inference.
The integration of visual data into the context window depends on the model’s design. Some architectures, like GPT-4V or LLaVA, interleave image embeddings with text tokens. For instance, when a user submits an image and a question about it, the image is first encoded into a sequence of embeddings. These embeddings are inserted into the input sequence at specific positions, such as before or after the text prompt. The LLM then processes the combined sequence, using its attention mechanisms to draw connections between visual and textual elements. This approach allows the model to answer questions like “Describe the chart in this image” by analyzing the embedded visual features and linking them to relevant concepts in the text.
However, there are practical challenges. Image embeddings can consume a large portion of the context window’s token limit, especially for high-resolution images. To address this, models often downsample images or use compression techniques. For example, a ViT might split an image into 16x16 pixel patches, reducing the total tokens needed. Additionally, training these models requires datasets with paired images and text to teach the LLM associations between visual and linguistic patterns. Despite these steps, LLMs don’t “see” images as humans do—they process abstract representations of visual features, which limits their ability to handle fine-grained details or spatial relationships beyond what the encoder captures. Developers working with multimodal LLMs must balance image resolution, token usage, and task requirements to optimize performance.