Text
Text is a fundamental data modality that serves as the primary input and output format for large language models (LLMs). In multimodal AI systems, text operates alongside vision data, audio, and other modalities, but remains the dominant channel through which LLMs communicate and reason. LLMs process text as sequences of tokens—discrete units representing words, subwords, or characters—which are converted into numerical representations that neural networks can manipulate.
Processing and Generation
The core mechanism of LLM operation involves encoding text tokens into embedding vectors, processing them through transformer layers, and decoding output tokens back into human-readable text. This token-based approach allows models to handle variable-length sequences and generate text one token at a time, with each new token conditioned on all previously generated tokens. The efficiency of this sequential processing has made text the standardized modality for training and deploying language models at scale.
Role in Multimodal Systems
While modern AI systems increasingly incorporate images, video, and audio, text typically remains the interface through which users interact with and receive outputs from multimodal models. Vision transformers and other modality encoders convert non-textual data into token-like representations that can be processed alongside or fed into text-based language models. This architectural pattern reflects both the historical dominance of text in deep learning and the practical advantage of using a single, well-optimized token-processing engine across diverse data types.
Source Notes
- 2026-04-10: What is Multimodal AI? How LLMs Process Text, Images, and
- 2026-04-07: Multimodal AI Concepts Approaches and Data Processing by LLMs · ▶ source