Welcome to today’s session! Today, we will delve into the roles of Tokenizers and Embeddings in Large Language Models (LLMs) and explore how they are trained. These two components are at the core of language models, and understanding their mechanisms and interrelationships is essential for grasping how LLMs operate. I will guide you through this step by step, aiming to clear up any confusion and provide a clear understanding.
I. General Process Overview
When training and using LLMs, text data typically goes through the following main steps:
1. Tokenization: Converting raw text into basic units (Tokens) that the model can process.
2. Embedding: Transforming Tokens into vector representations (Embeddings) for model computation.
3. Model Processing: The model processes the embedding vectors to generate output.
4. Decoding: Converting the model's output vectors back into readable text.
Your understanding is correct: Text is first converted into Tokens by the tokenizer, and then the Tokens are transformed into vector representations by the embedding layer.
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II. What is a Tokenizer?
1. Definition
A tokenizer is a tool that converts a text sequence into a sequence of Tokens. Tokens are the basic units that the model processes, which can be words, subwords, or even characters.
2. Functions
- Standardizing input: Converting diverse text into a unified format that the model can understand.
- Controlling vocabulary size: Through reasonable tokenization methods, the number of words the model needs to learn is controlled, balancing the model's capacity and performance.
3. Tokenization Methods
- Character-level tokenization: Text is split into individual characters. This is suitable for handling out-of-vocabulary words or special symbols, but results in longer sequences.
- Word-level tokenization: Text is split into words based on spaces or language-specific rules. For Chinese, which lacks spaces, special tokenization algorithms are required.
- Subword-level tokenization: Words are further split into smaller subword units. Common methods include:
- BPE (Byte Pair Encoding)
- WordPiece
- SentencePiece
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III. Training a Tokenizer
1. Training Objectives
- Building an efficient vocabulary: By analyzing large amounts of text, the most common and useful Tokens are identified.
- Balancing vocabulary size and representation capability: A larger vocabulary increases model complexity, while a smaller one reduces the model's ability to represent text.
2. Training Process
- Collecting a corpus: A large amount of text data covering various domains and styles.
- Frequency statistics: Calculating the occurrence frequency of different characters, subwords, or words.
- Applying tokenization algorithms: Based on algorithms like BPE or WordPiece, iteratively merging the most frequent characters or subwords to generate a vocabulary.
3. Differences Among Companies' Tokenizers
- Algorithm selection: Different companies may choose different tokenization algorithms based on their task requirements and language characteristics.
- Corpus differences: Different training corpora lead to different vocabularies and tokenization rules.
- Custom optimization: Tokenizers are specially customized for specific domains or applications.
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IV. What is Embedding?
1. Definition
Embedding is the process of mapping discrete Tokens into a continuous high-dimensional vector space. Each Token corresponds to a fixed-dimensional vector, known as the embedding vector.
2. Functions
- Dimensionality transformation: Converting symbolic representations that cannot be directly computed into numerical forms that the model can process.
- Capturing semantic information: Through training, semantically similar Tokens are positioned closer together in the vector space.
3. Types of Embeddings
- Static embeddings: Such as Word2Vec and GloVe, where the embedding vectors remain fixed after training.
- Dynamic embeddings: Such as embeddings in Transformer models, where the embedding vectors are updated during training and can adjust based on context.
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V. The Relationship Between Tokenizers and Embedding Layers
- Process connection: The output of the tokenizer (Tokens) is the input to the embedding layer.
- Independent training: Tokenizers and embedding layers are usually trained as separate modules.
1. Tokenizer Training
- Completed before model training: The tokenizer is trained on a large corpus before model training, generating a fixed vocabulary and tokenization rules.
2. Embedding Layer Training
- Trained with model parameters: During model training, the parameters of the embedding layer are updated together with the loss function optimization.
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VI. The Relationship Between Tokenizers and Model Architecture
- Model requirements for tokenization: Some model architectures may have requirements for input sequence length or format, which affects the choice of tokenization strategy.
- Positional encoding: Transformer architectures need to consider the position information of Tokens in the sequence, so positional encoding is added to the embeddings.
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VII. The Impact of Inaccurate Tokenizers
1. Impact on Model Input
- Semantic loss: If the tokenizer fails to correctly segment the text, it may lead to loss or ambiguity of semantic information.
- Unrecognized Tokens: Out-of-vocabulary words (i.e., words not in the vocabulary) may appear and need special handling (e.g., replacing with [UNK]).
2. Impact on Embedding
- Incorrect embedding representation: Tokens that are not correctly segmented may have embedding vectors that fail to effectively represent the original semantics.
- Model performance degradation: Overall, this reduces the model's ability to understand and generate text.
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VIII. Case Study: The Complete Process from Tokenization to Embedding
Assume the sentence: "Machine learning is a branch of artificial intelligence."
1. Tokenizer Processing
- Using a BPE tokenizer:
- Tokens: ["机", "器", "学习", "是", "人工智能", "的", "分", "支", "之", "一", "。"]
2. Embedding Layer Conversion
- Looking up embedding vectors for each Token:
- "机" → Vector E1
- "器" → Vector E2
- ...
- "。" → Vector E11
- Adding positional encoding:
- Final input vector = Embedding vector + Positional encoding
3. Model Processing
- Input sequence: \[E1, E2, ..., E11\]
- Transformer layer: Capturing relationships among Tokens in the sequence through self-attention mechanisms.
- Output: The model generates results based on the task (e.g., text generation, classification).
4. Tokenizer Error Scenario
- Incorrect tokenization: Suppose the tokenizer incorrectly segments the sentence into:
- ["机器学", "习是人", "工智能", "的分支", "之一。"]
- Impact:
- Unknown Tokens: For example, "机器学" may not be in the vocabulary.
- Semantic errors: Unreasonable segmentation leads to semantic misunderstandings.
- Embedding failure: Unable to find corresponding embedding vectors or the embedding vectors fail to correctly represent the meaning.
- Poor model output: The final model performance degrades and cannot correctly complete the task.
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IX. Differences in Tokenizers and Embedding Methods Among Companies
1. OpenAI's GPT Series
- Tokenizer: Uses BPE-based tokenization, considering byte-level representations to handle characters from multiple languages.
- Embedding layer: Trained together with the model, including positional encoding.
2. Meta's LLaMA Series
- Tokenizer: Likely uses SentencePiece, suitable for subword decomposition in multiple languages.
- Embedding layer: Also optimized together with model parameters and may be adjusted for specific tasks.
3. Reasons for Differences
- Task requirements: Different models target different application scenarios and tasks, leading to different tokenizer designs.
- Language support: Tokenization strategies need to be adjusted for different languages.
- Model architecture: Although both are based on Transformers, specific implementations and optimizations may differ, affecting embedding methods.
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X. Methods for In-depth Learning and Understanding
1. Theoretical Learning
- Reading classic papers:
- BPE: Understanding the principles of tokenization algorithms.
- Transformer: Understanding model architecture and embedding methods.
- Understanding embedding concepts:
- Word Embedding: Principles of Word2Vec, GloVe, etc.
2. Practical Exercises
- Using open-source tools:
- Hugging Face Transformers: Practicing tokenization and embedding processes.
- Training custom tokenizers: Trying to train tokenizers on different corpora and observing changes in the vocabulary.
- Comparative analysis:
- Effects of different tokenizers: Comparing results using different tokenizers on the same text.
- Visualization of embedding vectors: Observing the distribution of embedding vectors through dimensionality reduction methods (e.g., t-SNE).
3. Project Application
- Building a small language model: Training a simple language model from scratch to experience the entire process.
- Tuning the model: Trying to modify the tokenizer or embedding layer and observing the impact on model performance.
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XI. Practical Application Logic Understanding
1. Why are Tokenizers and Embedding Layers So Important?
- Foundation of data: The tokenizer determines the form of data the model sees, while the embedding layer determines how the model understands the data.
- Impact on model performance: Good tokenizers and embedding layers can improve the model's generalization ability and accuracy.
2. Design and Selection Strategies
- Choosing tokenization strategies based on tasks:
- Language characteristics: For example, Chinese is suitable for character-level or subword-level tokenization.
- Domain-specific: Special domains may require retaining specific vocabulary.
That concludes our comprehensive introduction to the roles, training methods, and relationship with model architecture of Tokenizers and Embeddings in Large Language Models. I hope this content helps you better understand the underlying mechanisms of language models. If you have any further questions or would like to explore related topics in more depth, feel free to ask. Thank you for reading, and I wish you continued progress in your learning and practice!