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Balkrishna Pandey
Balkrishna Pandey

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How Tokenization and Encoding Work in LLM?

Introduction: What are LLMs

Large Language Models (LLMs) are advanced AI systems designed to process, understand, and generate human language. They play a crucial role in various applications, including language translation, content creation, and more.

What is Tokenization?

Tokenization is the process of breaking down text into smaller, manageable units called tokens.

Tokens: Building Blocks of Text

  • Tokens can be words, characters, subwords, or symbols, depending on the type and size of the LLM.
  • For example, the sentence "Hello, world!" can be tokenized into ["Hello", ",", "world", "!"]. This step is essential because it helps in simplifying complex text, making it easier for models to analyze and interpret.
  • Each token is assigned a unique identifier that the model uses to represent it.
  • The model learns the relationships between tokens and uses them to generate text, translate languages, answer questions, and perform other tasks.

What is Encoding?

Encoding is the subsequent step where these tokens are converted into a numerical format that LLMs can process. Since machine learning models inherently operate on numbers, each token is mapped to a unique integer in a process known as encoding. For instance, in a simple model, the tokens ["Hello", "world"] might be encoded as [1, 2]. This numerical representation allows LLMs to perform operations like pattern recognition, language translation, and text generation.

Together, tokenization and encoding form the foundation of text processing in LLMs. They enable these models to break down and interpret human language, transforming raw text data into a format suitable for a wide array of complex language-based tasks.

How does the Tokenization process work?

Now we know the definition let's dive little bit deeper; during tokenization, the text is often converted into integers or numbers, but this process occurs in two stages:

1. Tokenization Stage: Initially, the text is broken down into smaller units or tokens. Depending on the tokenization method, these tokens could be words, subwords, or characters. At this stage, the text is still in its textual form, just split into smaller pieces.

2. Numerical Representation Stage: After tokenization, each token is mapped to a unique integer. This mapping process is known as encoding. This step is crucial because machine learning models, including neural networks, do not understand text directly; they operate on numerical data. The mapping typically uses a pre-defined vocabulary where each unique token is assigned a specific integer.

For example

Word-based tokenization:

The sentence "The quick brown fox jumps over the lazy dog" might be tokenized into the following word-level tokens:
The sentence is split into nine tokens

['The', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog']
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In the encoding stage, these tokens are converted into integers based on the vocabulary. For example, let's say our vocabulary is created as follows,

'The': 1,
'quick': 2,
'brown': 3,
'fox': 4,
'dog': 9
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in encoding, each token is replaced with its corresponding integer from the vocabulary. This results in the following encoded representation:

[1, 2, 3, 4, 5, 6, 1, 7, 8]
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Character-level tokens:

The same sentence might be tokenized into the following character-level tokens: ['T', 'h', 'e', ' ', 'q', 'u', 'i', 'c', 'k', ' ', 'b', 'r', 'o', 'w', 'n', ' ', 'f', 'o', 'x', ' ', 'j', 'u', 'm', 'p', 's', ' ', 'o', 'v', 'e', 'r', ' ', 't', 'h', 'e', ' ', 'l', 'a', 'z', 'y', ' ', 'd', 'o', 'g']

In this encoding stage, lets say our vocabulary is as follows

Alphabet (a-z): 1-26
Space: 27
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Our tokenization process looks like this,

T: 20
h: 8
e: 5
: 27
q: 17
u: 21
i: 9
c: 3
k: 11
: 27
b: 2
r: 18
o: 15
w: 23
n: 14
: 27
f: 6
o: 15
x: 24
: 27
j: 10
u: 21
m: 13
p: 16
s: 19
: 27
o: 15
v: 22
e: 5
r: 18
: 27
t: 20
h: 8
e: 5
: 27
l: 12
a: 1
z: 26
y: 25
: 27
d: 4
o: 15
g: 7
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Our final encoding sequence looks like this,

[20, 8, 5, 27, 17, 21, 9, 3, 11, 27, 2, 18, 15, 23, 14, 27, 6, 15, 24, 27, 10, 21, 13, 16, 19, 27, 15, 22, 5, 18, 27, 20, 8, 5, 27, 12, 1, 26, 25, 27, 4, 15, 7]
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Subword-level Tokenization

Subword tokenization involves breaking words into smaller, meaningful units (subwords). This method is particularly effective for handling rare or compound words.

For the sentence "The quick brown fox jumps over the lazy dog", a subword tokenizer might tokenize it as follows, assuming compound words and frequent subwords are recognized:

['The', 'quick', 'brow', 'n', 'fox', 'jump', 's', 'over', 'the', 'lazy', 'dog']
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In the encoding stage, if the vocabulary assigns unique integers to subwords as well, the encoding might look like this:

'The': 1,
'quick': 2,
'brow': 3,
'n': 4,
'fox': 5,
'dog': 11
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The encoded representation becomes:

[1, 2, 3, 4, 5, 6, 7, 8, 1, 9, 11]
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This encoding captures more information about the structure and morphology of the words than simple word-level tokenization.

Symbols-Based Tokenization

Symbols-based tokenization is often used in specialized text processing, such as programming languages or mathematical expressions, where symbols have specific meanings.

For example, in a mathematical expression like "x = 2 + 3", the tokenization might be:

['x', '=', '2', '+', '3']
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Assuming a vocabulary that includes both numbers and symbols, the encoding might look like this:

'x': 1,
'=': 2,
'2': 3,
'+': 4,
'3': 5
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The encoded sequence then becomes:

[1, 2, 3, 4, 5]
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This approach is crucial for contexts where symbols carry distinct meanings and are not just part of the textual content.

Q: How Are Tokens Mapped to Token IDs?
Tokens are mapped to token IDs using a pre-defined vocabulary where each unique token is assigned a unique numerical ID. This mapping is usually automated and often implemented as a lookup table.

The combination of these stages allows language models to process and understand textual data. The numerical representation (often integers) is what is actually fed into and processed by the machine learning algorithms. This conversion to numbers is essential for various operations such as embedding lookup, where each token's integer ID is used to find its corresponding vector in an embedding matrix, enabling the model to understand and generate meaningful responses based on the learned patterns in the data.

Q: What is the Difference Between Tokenization and Encoding?
Tokenization is splitting text into smaller, meaningful units (tokens), while encoding involves converting these tokens into a numerical format that machine learning models can process.

Numerical Representation (Encoding)

Why Integers?: Integers are a common choice for encoding tokens because they are efficient in terms of memory and computational resources. They serve as unique identifiers for each token.

During encoding, are Texts Always Converted to Integers or Numbers?

No, texts are not always converted to integers or numbers during encoding. While it is a common practice for some tasks and models, it is not universally applicable. The conversion process depends on the specific encoding technique used and the desired outcome. Here's a breakdown:

Cases where text is converted to integers or numbers:

  • Word-level encoding: In some cases, after splitting the text into words, each word might be assigned a unique integer using a vocabulary mapping. This is common for tasks like machine translation or language modeling.
  • Character-level encoding: Each individual character in the text is assigned an integer based on a predefined mapping, like the alphabet (a-z -> 1-26). This is useful for tasks that require detailed analysis of individual characters, like handwriting recognition.
  • Subword-level encoding: Words are broken down into smaller units like morphemes or syllables, and each is assigned an integer representation. This is helpful for handling complex or unknown words not covered in a standard vocabulary.
  • Feature-based encoding: Extracted features from the text, like named entities, parts of speech, or syntax dependencies, are represented as integers or other numerical formats. This allows models to analyze specific aspects of the language.

Cases where text is not converted to integers or numbers:

  • Word-level encoding: The tokens can simply be represented as strings, especially if the vocabulary size is manageable and the task does not require numerical processing.
  • Embedding techniques: Instead of integers, words can be represented as vectors in a high-dimensional space, capturing semantic meaning and relationships. This is commonly used in deep learning models for natural language processing.
  • Sparse representations: Techniques like one-hot encoding represent each word as a sparse vector, where only the corresponding index is filled with 1 and others remain 0. This can be useful for tasks like information retrieval or document similarity.
  • Sentence-level encoding: When the entire sentence is treated as a single token, it may not be converted to an integer if the model can directly handle text input.

Factors influencing the choice:

  • Task requirements: If the task involves calculations or needs numerical input for specific models, integer conversion might be necessary.
  • Model architecture: Neural networks and similar models typically require numerical representations for efficient processing.
  • Interpretability: While integer representations are efficient for processing, they can be less interpretable than raw text, which might be important for certain tasks.

Ultimately, the decision to convert text to integers or numbers during encoding depends on a balancing act between efficiency, interpretability, and the specific needs of the task and model.

Challenges Associated with Tokenization

While tokenization is a foundational aspect of natural language processing, it comes with its own set of challenges, especially when dealing with diverse writing systems and evolving languages.

Challenges with Different Writing Systems

One significant challenge arises from the varied nature of writing systems across languages. For instance, in alphabetic languages like English, tokenization is relatively straightforward due to the clear spaces between words. However, the story is different for logographic languages such as Chinese. In these languages, characters are often grouped together without intervening spaces to form words, making it difficult to determine where one word ends and another begins. This inherent ambiguity can lead to complexities in accurately tokenizing such texts.

Dictionary-Based Tokenization Limitations

A common approach to tackle tokenization in languages with complex scripts is dictionary-based tokenization. This method involves matching sequences of characters against entries in a known dictionary. While effective to a certain extent, it has its limitations. No dictionary is entirely comprehensive, especially when it comes to handling new, rare, or specialized words. As a result, dictionary-based approaches can struggle with out-of-vocabulary words, a frequent hurdle in dynamic language environments.

Supervised Sequence Labeling for Tokenization

To address these limitations, researchers have explored supervised sequence labeling for tokenization. This approach involves training a model on numerous examples, teaching it to identify appropriate points to split text into tokens. One implementation of this might involve using a logistic regression classifier that moves across a text, making independent segmentation decisions. However, this technique might not always be optimal since each decision is made in isolation, without a holistic view of the entire text.

Advanced Methods: CRFs and Neural Networks

More sophisticated methods have been developed to enhance tokenization accuracy. Techniques like Conditional Random Fields (CRFs) and neural networks offer more nuanced solutions. For example, the LSTM-CRF architecture integrates a type of neural network (LSTM) with CRFs, considering the entire sequence of characters for more informed tokenization decisions. The LSTM processes the character sequence, while the CRF component aids in making more accurate splits.

Optimizing Tokenization with Algorithms

The effectiveness of these advanced methods is further enhanced by algorithms like the Viterbi algorithm. This algorithm is instrumental in determining the most likely sequence of tokens, optimizing the tokenization process based on the model's predictions.

Vocabulary in Natural Language Processing (NLP)

Vocabulary in NLP refers to the set of unique words or tokens that a language model recognizes and processes. It's the foundation for text analysis, enabling models to convert text into numerical forms they can understand and manipulate.

The Creation Process

The creation of a vocabulary typically involves analyzing a large body of text (corpus) and identifying unique tokens. This process depends on the chosen tokenization method, which could be based on words, subwords, or characters. The goal is to create a comprehensive yet manageable set of tokens that represent the linguistic patterns of the corpus.

Considerations in Vocabulary Creation

  • Size and Scope: Balancing the size of the vocabulary with the diversity of the language is crucial. Larger vocabularies can capture more nuances but may be less efficient.
  • Language Specifics: The vocabulary should cater to the specific linguistic characteristics of the target language, such as handling compound words in German or script nuances in Arabic.
  • Tokenization Method: The choice between word-level, subword-level, or character-level tokenization significantly impacts the vocabulary's structure.

Pre-Defined Vocabulary in Pre-Trained Models

Many modern NLP models come with pre-defined vocabularies, created based on extensive training corpora. These vocabularies are tailored to the model's intended use and training data, offering a broad linguistic foundation for various applications.

Open vs Closed Vocabulary Systems

  • Open Vocabulary: Continuously adapts and adds new words, useful for evolving languages and specialized domains.
  • Closed Vocabulary: Fixed post-training; common in many pre-trained models. Includes a set number of tokens and often uses special tokens to handle out-of-vocabulary words.

Handling Unknown Words

A key challenge is dealing with words that aren't in the vocabulary (out-of-vocabulary, OOV words). Strategies include:

  • Special Tokens: Using tokens like [UNK] or [OOV] to represent unknown words.
  • Subword Tokenization: Breaking down unknown words into known subunits, allowing the model to infer meaning.


Why is Tokenization Important in NLP?

Tokenization is important because it breaks down text into manageable pieces that models can easily process, helping in understanding and generating language accurately.

Can Encoding Capture Semantic Relationships Between Words?

Advanced encoding methods like Word Embeddings and Contextual Embeddings can capture semantic relationships by representing words in a way that reflects their meaning and context.

Are There Any Challenges with Tokenization in Different Languages?

Yes, languages with different scripts or complex grammar (like Chinese or Arabic) pose challenges for tokenization, requiring more sophisticated methods than those used for languages like English.

How Does Contextual Encoding Differ from Traditional Encoding Methods?

Contextual encoding methods, like those used in BERT and GPT, generate word representations that consider the surrounding context, making them more effective for understanding language nuances than traditional methods like One-Hot Encoding or TF-IDF.

Is Manual Intervention Required in Tokenization and Encoding Processes?

Generally, no. Both tokenization and encoding are automated processes, especially in large-scale NLP models. Manual intervention is typically limited to the design and choice of tokenization and encoding strategies.

What Are the Token Counts for GPT-2, GPT-3, and GPT-4?

GPT-2 and GPT-3 have around 50,000 tokens in their vocabularies. The exact token count for GPT-4 was not publicly disclosed as of writing this blog, but it is likely similar.

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