Lecture Notes on Tokenization in Large Language Models

Introduction to Tokenization

• Tokenization is a crucial process in large language models (LLMs).
• It involves converting text into tokens (integers) that the model can process.
• Tokenization is often seen as complex and can lead to unexpected issues in LLM behavior.

Basic Concepts

What is Tokenization?

• Tokenization is the process of translating strings into sequences of tokens.
• In previous projects, a naive character-level tokenizer was used, which is simple but not efficient for LLMs.
• More sophisticated tokenization schemes are necessary for practical applications.

Character-Level vs. Byte Pair Encoding (BPE)

• The naive tokenizer created a vocabulary of 65 possible characters.
• BPE is a more advanced algorithm used to construct token vocabularies, allowing for more efficient encoding of text.
• Byte Pair Encoding is about merging frequently occurring pairs of characters or sub-strings into single tokens.

Tokenization Process

Training a Tokenizer

• Training involves creating a vocabulary from a training dataset and applying the BPE algorithm.
• The GPT-2 model uses a vocabulary of 50,257 possible tokens.
• Each token is associated with an embedding, which is trainable during model training.

Challenges and Complexities

• Issues arising from tokenization can lead to unexpected performance, especially in spelling, non-English languages, and arithmetic tasks.
• Example problems include:
  • LLMs struggle with spelling due to arbitrary tokenization of strings.
  • Non-English languages may be tokenized less efficiently, leading to bloated sequences.
  • Simple arithmetic tasks are hindered by the tokenization of numbers into arbitrary tokens.

Practical Demonstrations

Tokenization Tools

• The lecture introduced a web app for live tokenization demonstration (e.g., Tiktokenizer).
• Demonstrated how the same string can be tokenized differently depending on the tokenizer used (e.g., GPT-2 vs. GPT-4).

Token Examples

• Various examples were shown to illustrate how tokens are derived from input strings, including whitespace handling and digit tokenization.
• Observed specific cases of tokenization issues with examples like the string "end of text."

Improving Tokenization Strategies

Recommendations for Future Work

• Tokenization strategies can be refined for better performance in LLMs.
• Considerations include context length, vocabulary size, and character representation.
• Keep in mind special tokens for specific use cases (e.g., end-of-text tokens).

Advanced Techniques

• Future videos may cover improvements to tokenization methods, including removing tokenization altogether.
• The goal is to achieve efficient text processing for both training and inference.

Conclusion

• Tokenization is foundational for LLMs and requires careful consideration.
• Understanding its complexities is vital for improving LLM performance.
• Continuous research and refinement of tokenization processes will inform future LLM advancements.