Understanding Tokens in Large Language Models
This article explains what tokens are in the context of Large Language Models (LLMs) like ChatGPT, how they differ from words, and why they are important for understanding usage limits and context windows.
Why it matters
Understanding tokens is crucial for effectively using and managing costs of Large Language Models in applications and workflows.
Key Points
- 1Tokens are not the same as words - they are chunks of text that can be a whole word, part of a word, or punctuation
- 2Tokenization is the process of converting human-readable text into a sequence of numbers that the model can process
- 3Tokens determine usage limits and the model's context window or memory for a conversation
- 4Sending long conversation histories with each new message can quickly consume token limits
Details
The article explains that tokens are the fundamental units that Large Language Models (LLMs) like ChatGPT work with, rather than raw text. Tokenization is the process of breaking down text into these numerical tokens that the model can process. Tokens are not the same as words - a single word can be split into multiple tokens based on common language patterns. This allows the model to learn the meaning of common word parts (like 'un-') and use them efficiently. Tokens are important because they determine usage limits - free tiers and paid plans are priced based on the number of tokens used. Hitting token limits can cause the model to 'forget' earlier parts of a conversation. The article advises starting new conversations frequently to avoid this issue and keep the focus on the current task.
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