I asked ChatGPT for more information on how it processes tokens. ## Tell me about how you use tokens and patterns. I understand that if I say ‘Mary had a little..’, there’s a pattern identifiable from your training to complete it as ‘lamb’. But what happens when I talk freely, like now? How do you process that and return meaningful responses? Your message raises a crucial point about how large language models like me work. At the heart of my functioning are **tokens**, **patterns**, and **probabilities** — but the way these combine to handle both predictable and freeform input is more sophisticated than it might seem at first. ### 1. **Tokens: Your Words Become Chunks** I process all input as **tokens**, which are essentially units of text. These could be: - A whole word (`"apple"`) - A word fragment (`"un"`, `"happi"`, `"ness"`) - Or punctuation (`"."`, `"?"`, etc.) The sentence _“Mary had a little lamb.”_ is broken down into a string of tokens, usually between 6–8 in this case, depending on how the tokenizer splits them. ### 2. **Patterns: Statistical Associations Across Tokens** During training, I was exposed to an enormous number of token sequences in many contexts — from books to forums to academic papers. This exposure means I have learned statistical associations: - “Mary had a little…” → high probability of “lamb” - “The mitochondria is the…” → high probability of “powerhouse of the cell” These aren’t fixed rules but **probabilistic expectations** learned from data. ### 3. **Freeform Input: Pattern Recognition at Scale** When you speak more freely — as you did in your question — I still process it as a sequence of tokens. Even though your phrasing is original, the **underlying structure, grammar, and conceptual framing** resemble countless others I have seen in training. For example, your sentence: > “But what happens when I talk freely, like now? How do you process that and return meaningful responses?” …contains tokens and syntactic patterns that frequently co-occur in writing about language, thought, and AI interaction. My training enables me to: - Identify this as a **meta-question** about language model processing - Recognise relevant **semantic patterns** (e.g. “talk freely” → freeform input; “return meaningful responses” → output generation) - Recall examples from training where similar questions were asked - Use those patterns to generate a **coherent, context-sensitive reply** ### 4. **Contextual Embedding and Attention** My architecture (transformers) does not just process tokens sequentially. It uses **self-attention**, meaning every token in your prompt can influence how I interpret and generate each subsequent token. In freeform conversation: - I form an internal representation of the **meaning and intent** of your entire message - I compare this against the vast distribution of patterns I’ve seen - I produce a response by predicting **one token at a time**, using the full preceding context This is why even open-ended or original input can still be understood and responded to — because meaning arises from **statistical regularities**, not exact repetition. ### 5. **Important Limitation** I do not **understand** meaning as humans do. I detect and reproduce **patterns of use**. My responses feel meaningful because they reflect how humans write when expressing meaning — not because I have awareness or comprehension. ![[ChatGPT_Token_Processing.png]] ## Can you be even more specific about how you process that pattern of tokens? Yes — I can give a more detailed account of how the pattern of tokens is processed inside a transformer model like GPT-4, step by step. This focuses on the **mechanics of pattern recognition and response generation**. I’ll take the sentence: > _"When I talk freely, like now?"_ …and explain how it is processed. ## 1. **Tokenisation** The first step is to break the sentence into tokens using a predefined vocabulary. Example tokenisation (approximate – depends on the specific tokenizer): |Text|Token| |---|---| |"When"|4521| |" I"|314| |" talk"|9220| |" freely"|6711| |","|11| |" like"|376| |" now"|1135| |"?"|30| These numbers are token IDs — pointers into a vocabulary of ~50,000 items. ## 2. **Embedding Layer** Each token ID is mapped to a **vector** (a long list of numbers, e.g. 1280 dimensions). This gives each token a numerical representation. For example: - "freely" → [0.14, -0.22, ..., 0.07] - "talk" → [0.05, 0.17, ..., -0.02] These vectors are **not static**. They are learned during training to capture semantic and syntactic similarities (e.g. "freely" and "openly" end up close in vector space). ## 3. **Positional Encoding** Because the model sees inputs as a set, not a sequence, we add **positional encodings** so that the model knows where each word occurs in the sentence. Without this, it wouldn't know if "talk" comes before or after "freely". So the model now has: - Token embedding + positional embedding = full input vector per token ## 4. **Self-Attention Mechanism** This is where patterns are identified dynamically. Each token can “attend to” every other token in the sentence — with different weights. For example: - “talk” might attend strongly to “freely” and “I” - “now?” might attend back to “when” to resolve the structure This attention is computed through **dot products** between token vectors that have been transformed into: - Queries (Q) - Keys (K) - Values (V) Attention(Q, K, V) = softmax(Q · Kᵀ / √d) · V This determines: - **Which words influence each other** - **How much each token should be updated based on others** The self-attention mechanism captures **local and global dependencies** — such as: - Subject–verb pairings - Logical modifiers ("freely" modifies "talk") - Sentence type (question, statement, etc.) ## 5. **Feedforward Layers** After self-attention, the model passes each token vector through a fully connected neural network. This helps build up more complex representations of meaning — layer by layer. This process repeats **across multiple layers** (96 in GPT-4), where each layer refines the internal pattern structure. ## 6. **Pattern Prediction: Next Token** Once the input has been fully processed, the model outputs a **probability distribution** over the entire vocabulary for the _next token_. For example, it might determine - 63% chance of "you" - 21% chance of "what" - 3% chance of "are" - etc. It picks the most probable (or samples from the distribution) and appends it to the sequence. This becomes part of the new context, and the process repeats — one token at a time. ## 7. **Emergent Structure Recognition** The remarkable part is that over many layers, the model: - **Builds semantic understanding** from token patterns - **Predicts structure and intent** without formal rules - **Learns abstract relationships** (e.g. cause–effect, temporal flow, conversational turns) So even freeform, unstructured language can be **matched to learned latent patterns**, allowing the model to produce a coherent, relevant response. ### Worked example Here is a **worked example** using your sentence: > _“When I talk freely, like now?”_ This will demonstrate how **self-attention** distributes weights across tokens — i.e. how the model decides which words influence each other during processing. We’ll walk through a **single self-attention head** (in reality, there are many per layer) and illustrate how it might assign attention weights from the token **"talk"** to other tokens in the sentence. ### Step 1: Tokens and Positions |Position|Token| |---|---| |0|When| |1|I| |2|talk| |3|freely| |4|,| |5|like| |6|now| |7|?| Now let’s examine how **“talk” (position 2)** might attend to other tokens. ### Step 2: Sample Attention Weights (from the token “talk”) |Token|Weight (%)|Interpretation| |---|---|---| |When|10%|Provides temporal context| |I|20%|Establishes subject (“who talks”)| |talk|—|Self-attention weight typically reduced| |freely|30%|Direct modifier (adverb describing “talk”)| |,|2%|Structural punctuation| |like|15%|Introduces a comparative clause| |now|20%|Establishes immediacy (time context)| |?|3%|Sentence form cue (question)| In this simplified example: - “freely” and “I” are **semantically important modifiers** to “talk” - “now” and “like” contribute **contextual framing** - “when” hints at the **conditional or temporal aspect** These weights are used to **combine the representations** of those tokens with the vector for “talk”, refining its meaning through context. ### Step 3: Vector Update via Attention The model then computes a **weighted sum** of the surrounding token vectors, using the weights above. This new vector becomes the updated representation of “talk” — enriched with meaning from the full sentence. The same happens **for every other token**, with each attending to all the others. This mutual interaction builds deep interdependencies across the sentence, allowing even vague or original input to be fully interpreted. ### Step 4: Higher-Level Pattern Recognition After several layers: - The model learns that the **phrase as a whole is a question** - It sees that “talk freely, like now?” is a **meta-comment on communication** - It infers that the broader intent is **asking how the model processes unpredictable language** These abstract patterns aren't hardcoded but **emerge** from the training process — because similar sentence structures often appear alongside similar topics or intents in training data.