Tokens are fundamental units of text that language models use to process and generate language. Here are some specific examples of how tokens are used in language models:
1. Tokenization in NLP
Tokenization is the process of breaking down a piece of text into individual words or "tokens." This is a crucial step in natural language processing (NLP) as it helps computers understand and process human language by splitting it into manageable units.
Word Tokenization: This method splits text into individual words. For example, the sentence "I love ice cream" would be tokenized into three tokens: "I," "love," and "ice cream".
Character Tokenization: This method splits text into individual characters. For example, the word "smarter" would be tokenized into "s-m-a-r-t-e-r".
Subword Tokenization: This method splits text into subwords, which are smaller than words but bigger than characters. For example, the word "smarter" might be tokenized into "smart-er".
2. Token Usage in AI Models
AI models like ChatGPT consume tokens based on the number of characters and spaces in the input and output text. The number of tokens consumed depends on the specific AI model being used.
Language models have specific token limits to ensure efficient processing and memory usage. Exceeding these limits can lead to errors or inefficiencies.
Token Limits: For instance, GPT models have token limits such as 4k, 8k, or 32k tokens per interaction. If you use 1000 tokens per interaction, you can have multiple interactions within the total token limit.
Cost Optimization: Strategies to optimize token usage include concise prompt engineering, avoiding summarization of previous conversations, and requesting more efficient output formats like short bullets or tables.
4. Tokenization in Payment Processing
Tokenization is also used in payment processing to enhance security by replacing sensitive data like credit card numbers with unique tokens.
Tokenization is used to keep sensitive data private by transforming it into tokens that can be securely stored and used without exposing the original data.
In language modeling, tokens are used to create organized representations of language, which are useful for tasks like text generation and machine translation.
These examples illustrate the diverse applications of tokens in language models, from basic text processing to advanced AI applications and data security measures.
