Text
Text serves as the fundamental data modality for large language models (LLMs), functioning as both the primary input and output format in AI systems. While multimodal architectures increasingly integrate vision, audio, and other data types, text remains the dominant channel for model communication, reasoning, and instruction following. It provides the structured linguistic framework necessary for complex logical operations and semantic understanding within neural networks.
The processing of text involves converting raw character strings into sequences of tokens, which are discrete units representing words, subwords, or characters. These tokens are subsequently transformed into numerical embeddings that allow neural networks to manipulate linguistic data mathematically. This tokenization and embedding process enables models to perform critical tasks such as next-token prediction, classification, and generative text synthesis by identifying patterns and relationships within the numerical representations.
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