Tokenization is a critical preprocessing step in the workflow of large language models (LLMs). It involves converting raw text into a sequence of tokens that the model can process. Each token corresponds to an integer, which is then used to retrieve a vector from an embedding table. This vector representation of the token is what feeds into the transformer layers of the LLM.
Encoding is the process of converting text into tokens. The encoding process typically starts with the raw text as a string of Unicode code points. This string is then encoded into bytes using a UTF-8 encoding scheme, resulting in a byte stream that represents the original text.
The byte stream is then tokenized into integers using a tokenizer that has been trained using an algorithm like Byte Pair Encoding (BPE). BPE works by iteratively merging the most frequent pairs of bytes or characters in the training data until the desired vocabulary size is reached.
Here’s a simplified example of how encoding might work in Python:
# Assuming 'text' is our input string and 'tokenizer' is our trained tokenizer
encoded_tokens = tokenizer.encode(text)
The encoded_tokens would be a list of integers representing the tokenized version of the input string.
Decoding is the reverse process of encoding, where a sequence of tokens is converted back into a string. This process involves looking up each token in the embedding table to retrieve the corresponding byte or character sequence. These sequences are then concatenated and decoded from UTF-8 back into a Unicode string.
Here’s an example of how decoding might work:
# Assuming 'tokens' is our sequence of integers representing tokens
decoded_text = tokenizer.decode(tokens)
The decoded_text would be the original string reconstructed from the tokenized representation.
In addition to the tokens derived from the text, LLMs often use special tokens to indicate the start or end of a sequence, padding, unknown words, or other control features. These special tokens are added to the tokenizer’s vocabulary and are used to structure the input and output in a way that the model can understand.
The encoding and decoding processes are fundamental to training and using LLMs. They allow the model to handle text as discrete units of information, which are then used for various tasks such as text generation, translation, or classification.
When building a tokenizer, it’s essential to consider the complexity of the tokenization process, the desired vocabulary size, and how the tokenizer will handle unseen or rare characters. The tokenizer’s performance can significantly impact the overall effectiveness of the LLM.
A live demonstration of tokenization can be insightful for understanding how different strings are tokenized. Using web applications like tiktokenizer.versal.app, users can interactively type text and observe how it is tokenized in real-time. These demonstrations can reveal how English sentences, arithmetic, non-English languages, and programming languages are tokenized differently by the model, highlighting the importance of a well-designed tokenizer in the performance of LLMs.
Encoding and decoding are not just technical details but critical components that can significantly influence the performance and capabilities of LLMs. A deep understanding of tokenization can help in designing better models and improving their ability to handle a wide range of language processing tasks.