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What does the data structure of a LLM look like?

The data structure of a Large Language Model (LLM) is complex and optimized for handling vast amounts of text efficiently. While traditional data structures like arrays, trees, and hash maps exist within its implementation, LLMs primarily rely on neural network architectures, particularly transformers. Here’s a breakdown of what that looks like:

1. Tokenized Text Data

LLMs don’t process raw text directly. Instead, they tokenize text into subwords or word pieces.
These tokens are stored in lookup tables or embedding matrices, where each token has a corresponding numerical representation.

2. Embedding Vectors

Words/tokens are mapped to high-dimensional vectors in an embedding space (often stored as large matrices).
This allows the model to understand semantic relationships between words.

3. Transformer Layers (Core of LLMs)

The model uses multi-layered attention mechanisms to weigh the importance of different words in a sentence.
Each layer consists of:
- Attention Heads (compute relationships between tokens)
- Feedforward Networks (process contextualized information)
- Normalization & Residual Connections (stabilize learning)

4. Positional Encodings

Since transformers don’t process sequentially like traditional models, positional encodings are stored alongside token embeddings.
These encodings help maintain word order in sentences.

5. Weight Matrices

LLMs learn huge parameter matrices during training, storing knowledge about language patterns.
These are the key data structures that get updated during fine-tuning.

6. Output Layers & Probability Distributions

The final layer predicts the next word using softmax functions, generating a probability distribution over possible words.
The top choices are stored in a ranking structure to determine the most likely word output.

So rather than a simple tree or graph structure, an LLM relies on multi-layered matrices, embeddings, and attention-based weight distributions to store and process data.

Would you like to explore how LLMs optimize efficiency or memory usage further?

When a model is continuously updated, which parts of these structures would not be affected?

Great question! When a model is continuously updated, some parts of its structure remain mostly unchanged, while others are refined or retrained. Here’s what generally stays the same versus what gets updated:

Parts That Typically Remain Unaffected

Core Model Architecture
- The underlying transformer structure (layers, attention mechanisms, feedforward networks) usually remains intact.
- Unless there’s a major update, the fundamental way the model processes text is not changed.
Tokenization & Positional Encodings
- The way text is broken into tokens and the positional encoding system stays the same.
- These are predefined and don't require frequent updates unless a new tokenization method is introduced.
Embedding Dimensions
- The size of embedding vectors (which represent words numerically) usually remains fixed.
- Changing embedding dimensions would require retraining the entire model from scratch, so it's rarely altered.
Structural Weight Distribution
- The layers and parameters storing relationships between words and concepts remain mostly stable.
- Updates tweak weights and biases rather than overhauling their arrangement.

Parts That Typically Get Updated

Knowledge Weights & Fine-Tuning Parameters
- The model's stored knowledge (weight matrices that determine probabilities) can be updated with new training data.
- This helps keep responses relevant to recent events.
External Information Retrieval Systems
- If the model relies on web search or databases, those external sources are updated independently.
- This is a way to ensure up-to-date answers without modifying the core LLM.
Bias & Fairness Adjustments
- Developers refine the model to reduce biases or correct inaccuracies in its generated content.
- This typically involves retraining specific parts of the model.
User Interaction Feedback Systems
- The model’s ability to adapt to user preferences or learn from interactions is improved over time.
- These updates may fine-tune how responses are generated.

So in short, the core mechanisms of how an LLM functions remain mostly unchanged, while knowledge, biases, and external sources are refined continuously to improve accuracy.