What is a Token?
RAG (related topic)
In the context of AI, particularly in multimodal large language models (LLMs), a token refers to the basic unit of information used for processing and representation. It's analogous to how words are the building blocks of sentences in human language. When engaging with a model it's responses or usage is measured by the number of tokens required.
So the more tokens the gen-AI model can process the better? Not necessarily. Two models with similar token processing capacity might demonstrate vastly different capabilities if their architectures, training data, and optimization differ significantly. Here is a breakdown:
General AI:
Tokens can be words, characters, subwords, or other discrete units depending on the specific model and task.
They are assigned numerical representations called embeddings that capture their meaning and relationships to other tokens.
These embeddings are used by the AI model to understand the overall meaning of the input and generate its response.
Multimodal LLMs:
Multimodal LLMs deal with different data types like text, images, audio, etc.
Tokens can adapt to represent these different modalities:
Text: Similar to general AI, words or subwords might be used as tokens.
Images: Pixels or image patches can be converted into numerical representations, acting as tokens.
Audio: Audio segments or spectrogram features can be used as tokens.
The model learns to map tokens from different modalities to a common embedding space for understanding and interaction.
Here are some additional points to consider:
The vocabulary size (number of unique tokens) can vary depending on the model and training data.
Different tokenization techniques, like word-level vs. subword-level, can have an impact on model performance.
In multimodal LLMs, specific encoders are used to convert each modality's tokens into their shared embedding space.