Tokenization4 articles

Tokenization

Articles

Byte-Pair Encoding (BPE) and Modern Subword Tokenization: Mathematical Foundations, Merge Dynamics, Byte-Level Encodings, and Vocabulary Compression Mechanics
Byte-Pair Encoding (BPE) and Modern Subword Tokenization: Mathematical Foundations, Merge Dynamics, Byte-Level Encodings, and Vocabulary Compression Mechanics Tokenization is the discrete boundary interface between raw textual sequences and the continuous vector representations of autoregressive large language models. Before an attention block computes a single inner product or a feed-forward network applies an activation function, an input string must be mapped into an ordered sequence of disc
August 28, 20261 min
Byte-Pair Encoding (BPE) and Modern Subword Tokenization: Mathematical Foundations, Merge Dynamics, Byte-Level Encodings, and Vocabulary Compression Mechanics
Byte-Pair Encoding (BPE) and Modern Subword Tokenization: Mathematical Foundations, Merge Dynamics, Byte-Level Encodings, and Vocabulary Compression Mechanics Tokenization is the discrete boundary interface between raw textual sequences and the continuous vector representations of autoregressive large language models. Before an attention block computes a single inner product or a feed-forward network applies an activation function, an input string must be mapped into an ordered sequence of disc
August 27, 20261 min
Byte-Pair Encoding (BPE): Mathematical Foundations, Merge Table Dynamics, Byte-Level Fallbacks, and Vocabulary Compression in Large Language Models
Every large language model operates on discrete numerical tokens rather than raw text strings. The translation layer between human language and model tensor activations is governed by subword tokenization algorithms. Among these, Byte-Pair Encoding (BPE) has emerged as the dominant standard across modern foundation models, including OpenAI's GPT-4, Meta's Llama 3, and Alibaba's Qwen 2.5. Originally developed by Philip Gage (1994) for data compression, BPE was adapted for natural language proces
August 26, 20261 min
Byte-Pair Encoding in Large Language Models: How Tokenizers Compress Text, Shape Context, and Fail
Large language models do not process strings directly. Before a single attention weight or linear projection executes, incoming text is converted into a sequence of discrete integers known as tokens. The choice and implementation of the tokenization algorithm establish the model's fundamental vocabulary, define the boundaries of its context window, dictate inference speed, and introduce unique behavioral quirks. Across modern transformer architectures, subword tokenization via Byte-Pair Encodin
August 18, 20261 min

Tokenization

Byte-Pair Encoding (BPE) and Modern Subword Tokenization: Mathematical Foundations, Merge Dynamics, Byte-Level Encodings, and Vocabulary Compression Mechanics

Byte-Pair Encoding (BPE) and Modern Subword Tokenization: Mathematical Foundations, Merge Dynamics, Byte-Level Encodings, and Vocabulary Compression Mechanics

Byte-Pair Encoding (BPE): Mathematical Foundations, Merge Table Dynamics, Byte-Level Fallbacks, and Vocabulary Compression in Large Language Models

Byte-Pair Encoding in Large Language Models: How Tokenizers Compress Text, Shape Context, and Fail