Understanding Text-to-Vector Representation: From Tokenization to Embeddings

Author
Allen Shaing
· 4 min read

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Overview

Recently I’ve been studying a lot of foundational LLM(large language models) architecture, I had several questions about the preprocessing required for large language models, so I began asking myself a lot of them. This page is meant to offer a more accessible explanation of how text is transformed into vectors. While techniques like Word2Vec exist, the text itself still needs to be processed before it can be converted into vectors, right? In the end, we’ll explore how this process intuitively fits into language models and what the overall concepts and transformations look like. This will be an exploration of first principles. I hope you find it helpful.

1. Tokenization

Tokenization is the first step in a language model's processing of text, where the text is broken down into smaller units called tokens. These tokens can be words, subwords, or characters, depending on the tokenization algorithm used.

  • Common Tokenization Methods: These include Byte Pair Encoding (BPE), WordPiece, and SentencePiece, which are used to split the text into word or subword units. Subword segmentation can effectively handle out-of-vocabulary (OOV) words.
  • Special Tokens: During tokenization, special tokens such as [CLS] (indicating the beginning of a sentence) and [SEP] (used for sentence separation) are added to handle specific structural information in tasks.

2. Embedding Matrix and Positional Encoding

The role of the embedding matrix is to convert each token into a fixed-dimensional vector. Each token has a unique index, and the model retrieves the corresponding vector from the embedding matrix using that index.

  • Vocabulary Size and Embedding Dimension define the size of the embedding matrix. The model looks up and returns a fixed-dimensional vector for each token.
  • Positional Encoding: In models like the Transformer, the self-attention mechanism does not retain sequence positional information. Therefore, positional encoding is used to provide positional information to the model. It can be implemented using sinusoidal and cosine functions to generate fixed encodings, or the positional information can be learned during training.

3. Training and Optimizing Embedding Vectors

The model continually optimizes the vectors in the embedding matrix through pre-training tasks and downstream tasks, enabling it to better capture the semantic and contextual relationships between words.

  • Pre-training tasks: In models like BERT, these tasks include Masked Language Model (MLM) and Next Sentence Prediction (NSP), while GPT uses an Autoregressive Language Model. These tasks help the model learn rich language representations.
  • Fine-tuning: During specific tasks, the embedding matrix and other model parameters are further adjusted to meet the specific task's requirements.

4. Contextualized Embeddings

Modern language models generate contextualized embeddings, meaning the same word will have different vector representations depending on the context in which it appears.

  • Self-Attention Mechanism: Through the self-attention mechanism, the model can dynamically generate a context-based representation for each word. The word's vector representation changes based on its position and meaning within the entire sentence.
  • Multi-layer Transformer Structure: The embedding vectors are progressively updated across multiple layers of the Transformer, incorporating increasingly complex semantic information from the context.

5. Differences Between Pre-trained and Static Embeddings (e.g., Word2Vec)

Unlike earlier methods such as Word2Vec, modern language models (such as GPT and BERT) learn embeddings directly through pre-training tasks, rather than relying on pre-trained static embeddings.

  • Static Embeddings in Word2Vec: Word2Vec generates static embeddings, meaning that a word's vector representation is fixed regardless of the context in which it appears.
  • Contextualized Embeddings: Modern language models dynamically adjust the embedding vectors based on the context, enabling them to handle polysemy and context-dependent word meanings.

Summary: Text-to-Vector Process (Revised)

  1. Tokenization: Splitting the text into tokens, which may include special tokens, using appropriate tokenization algorithms (e.g., BPE, WordPiece).
  2. Embedding Matrix and Positional Encoding: The embedding matrix looks up the vector representation for each token, and positional encoding provides positional information for the tokens in the sequence, complementing the self-attention mechanism's inability to handle order.
  3. Training and Optimizing Embedding Vectors: The model continuously refines the embedding vectors through pre-training tasks and fine-tuning to capture semantic relationships in context.
  4. Contextualized Embedding Vectors: Modern models use self-attention to dynamically generate context-aware embeddings for each word, allowing for better understanding of polysemy and words in different contexts.
  5. Comparison Between Static and Contextualized Embeddings: Traditional static embeddings (e.g., Word2Vec) are being replaced by contextualized embeddings in modern language models, which capture more complex language phenomena.

Comprehensive Conclusion

The embedding matrix is a key component of language models, as it translates discrete tokens into continuous vector representations. Modern language models, using the Transformer architecture and self-attention mechanisms, dynamically generate contextualized embeddings that better capture the semantic shifts of words in different contexts. This contextual embedding approach, compared to static word embeddings, offers greater flexibility and enhances the model’s language understanding capabilities, allowing it to handle complex language tasks and produce more accurate results.

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