GPT-2 Text Encoding Explained | Generated by AI
That’s a great set of questions about how Large Language Models (LLMs) like GPT process text. You’ve correctly identified the two main steps: encoding (tokenization) and embedding.
Here’s a breakdown of GPT-2’s encoding, its comparison to Word2Vec, and why LLMs need to encode text first.
🔢 GPT-2 Encoding: Text to Number
GPT-2 uses a tokenization method called Byte Pair Encoding (BPE), specifically a byte-level version. Its purpose is to convert raw text into a sequence of numbers, which are called token IDs, for the model to process.
How it Works
- Initial Vocabulary: The process starts with a base vocabulary consisting of all single bytes (256 characters) that appear in the training data (a vast corpus of text). This guarantees that any text (in any language/script) can be encoded, even if it contains completely new or rare characters, by breaking it down to its byte level.
- Iterative Merging (Training Phase):
- The tokenizer repeatedly scans the entire training text to find the most frequently occurring pair of adjacent bytes/tokens.
- This pair is merged into a single, new token, and this new token is added to the vocabulary.
- This step is repeated thousands of times (GPT-2 has a vocabulary of 50,257 tokens) until the desired vocabulary size is reached.
- Tokenization (Usage Phase): When you give the model a new sentence, the tokenizer uses the learned vocabulary of tokens and merges. It breaks the text down into the longest possible subword units it can find in its vocabulary.
The Result: Subword Units
This subword approach strikes a balance between:
- Character-level: Too long sequences, hard for the model to capture meaning.
- Word-level: Too many vocabulary words, and can’t handle out-of-vocabulary (OOV) words (like typos or new names).
BPE creates tokens that are:
- Whole common words (e.g., “the”, “a”, “is”)
- Common word parts (subwords) (e.g., “ing”, “tion”, “un”)
- Rare words are broken down into smaller, known subwords (e.g., “unbelievable” $\rightarrow$ “un” + “believ” + “able”). This prevents OOV words and is efficient.
🆚 Encoding vs. Word2Vec: Key Differences
You are correct that both are trained on text and both convert text into a numerical format, but they serve different roles and produce different outputs:
| Feature | GPT-2 Encoding (BPE Tokenization) | Word2Vec / GloVe |
|---|---|---|
| Output Type | Token ID (A single, unique integer for a token) | Word Embedding (A dense vector of floating-point numbers) |
| Purpose | Tokenization: To break text into manageable subword units and map them to a unique integer ID. This is the first step for an LLM. | Embedding: To represent the meaning of a word as a static vector in a way that similar words have similar vectors. |
| Contextual | No (The ID for “bank” is the same regardless of context). | No (The vector for “bank” is the same whether it means river bank or financial bank). |
| Handling OOV | Excellent. It can break down any unknown word into known subwords/bytes. | Poor. Typically assigns an “Unknown” vector or needs character-level extensions like FastText. |
🧠 Why Encode Text First If LLMs Have Embeddings?
This is a fundamental question about how LLMs work! You are right that LLMs have an embedding layer, but the process has two distinct steps:
1. Encoding (Tokenization: Text $\rightarrow$ ID)
The transformer architecture (like GPT) is a neural network that only works with numbers. It cannot process the string “cat” directly.
- Text (string) $\rightarrow$ Tokenizer $\rightarrow$ Token ID (integer)
- Example: “Hello world” $\rightarrow$
[15496, 995](Two integers)
The token IDs serve as lookup keys.
2. Embedding (Embedding Layer: ID $\rightarrow$ Vector)
The LLM’s Embedding Block is essentially a massive, trainable lookup table (a matrix) where the token ID is used as the index (row number).
- Token ID (integer) $\rightarrow$ Embedding Layer Lookup $\rightarrow$ Embedding Vector (floating-point vector)
- Example:
995(The ID for “ world”) $\rightarrow$ Look up row 995 in the embedding matrix $\rightarrow$[0.45, -0.12, 0.99, ...](A vector of, say, 768 numbers).
Why Not Just Use Random Numbers for Input?
If you were to initialize your input with random numbers without tokenization, you’d run into severe problems:
- Arbitrary/Loss of Meaning: Computers are pure calculation machines. If you just assign “the” the random number 5 and “cat” the random number 42, those numbers have no inherent connection or meaning that the model can start with.
- Vocabulary Management: How would you represent “apple” vs. “apples”? Or the typo “applle”?
- With tokenization, they become separate token IDs, and the model learns their distinct (but related) meanings.
- Without tokenization, you’d have no standardized, universal way to map every possible string of characters to an input the model could repeatedly recognize.
The token ID (the integer) is a discrete, unique identifier for a learned unit of text. The embedding vector is the continuous, meaningful numerical representation of that identifier, which is learned during the massive training process. The model’s training is what adjusts the values in the embedding matrix so that similar tokens (like “cat” and “dog”) end up with similar vectors.
Would you like to see a more detailed, step-by-step example of how Byte Pair Encoding (BPE) creates tokens from a sample text?