When a language model (LLM) is trained, each word is assigned a unique numerical code, known as an embedding, which encapsulates its meaning and relation to other words based on extensive training data. Typically, this embedding consists of a 4,096-dimensional list of numbers, allowing models to interpret nuances and contextual meanings. The choice of 4,096 dimensions, a power of two, balances capability and efficiency, offering optimal performance while avoiding the costs of larger models. As seen with OpenAI’s GPT-4.5, larger models can process complex emotional cues in language, enhancing understanding of subtle conversational patterns. The high-dimensional space where words are encoded means similar words are positioned close together; for instance, “table” and “chair” cluster more closely than “astronaut.” This intricate structure serves to compress vast amounts of information, enabling LLMs to perform remarkable tasks while remaining challenging to fully comprehend. This sophisticated numerical representation is key to their functionality.
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