• 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.

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