Tokenization strategy significantly affects the performance of large language models (LLMs) by determining how text is represented and processed. Here's how it impacts performance:
- Vocabulary Size: A smaller vocabulary (e.g., byte pair encoding) leads to fewer tokens, reducing computational cost, but risks losing semantic meaning.
- Granularity: Fine-grained tokenization (e.g., subword or character-level) handles rare words better but requires more tokens, increasing computation.
- Context Handling: Tokenizers that handle context effectively can improve the model's understanding of long-range dependencies and reduce the risk of ambiguity.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Vocabulary and Granularity: Balancing vocabulary size and granularity optimizes token usage and model efficiency.
- Contextual Awareness: A strategy that captures subwords or characters can handle out-of-vocabulary terms better, improving performance.
- Efficiency: Proper tokenization ensures better memory usage and faster training/inference.
Hence, a well-designed tokenization strategy improves the model's ability to capture semantic meaning and handle diverse inputs efficiently.
