In the world of LLMs, someone eventually pays for “tokens.” But tokens are not necessarily equivalent to words. Understanding the relationship between words and tokens is critical to grasping how language models like GPT-4 process text.
While a simple word like “cat” may be a single token, a more complex word like “unbelievable” might be broken down into multiple tokens such as “un,” “believ,” and “able.” By converting text into these smaller units, language models can better understand and generate natural language, making them more effective at tasks like translation, summarization, and conversation.
Continue reading