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Kozosvyst Stas (StasX)
Kozosvyst Stas (StasX)

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How Large Language Models (LLMs) Work: A Complete Overview

#nlp #ai #deeplearning #llm

How Large Language Models (LLMs) Work: A Complete Overview

Large Language Models (LLMs) are a type of artificial intelligence designed to understand, generate, and manipulate human language. They are built on deep learning architectures and trained on massive datasets to perform a wide range of natural language processing (NLP) tasks.

1. Architecture

Most LLMs are based on the Transformer architecture, introduced in the paper “Attention is All You Need” (Vaswani et al., 2017). The core components are:

  • Encoder: Processes input sequences and generates contextual representations.
  • Decoder: Generates output sequences from these representations.
  • Self-Attention Mechanism: Allows the model to weigh the importance of each word relative to others in a sequence.
  • Feedforward Neural Networks: Apply transformations to the representations at each layer.

Modern LLMs like GPT use only the decoder stack to predict the next token in a sequence.

2. Tokenization

Before training, text data must be converted into a numerical format:

  • Tokenization: Splitting text into smaller units (tokens), which can be words, subwords, or characters.
  • Vocabulary: Each token is mapped to a unique ID.
  • Embeddings: Tokens are converted into dense vectors that capture semantic meaning.

Popular tokenization methods include Byte-Pair Encoding (BPE) and WordPiece.

3. Training Process

Training an LLM involves two major stages:

a. Pretraining

  • The model learns general language patterns from massive text corpora.
  • Objective: Predict the next token in a sequence (causal language modeling) or fill in missing tokens (masked language modeling).
  • Uses gradient descent and backpropagation to adjust billions of parameters.

b. Fine-tuning

  • The model is further trained on domain-specific datasets or tasks.
  • Can be supervised (using labeled data) or reinforcement learning-based (e.g., RLHF - Reinforcement Learning from Human Feedback).

4. Attention Mechanism

The attention mechanism is the backbone of LLMs:

  • Query, Key, Value vectors (Q, K, V) are computed for each token.
  • Attention scores are calculated as:

Attention(Q, K, V) = softmax(QK^T / √d_k) V

where d_k is the dimensionality of the key vectors.

  • Enables the model to capture long-range dependencies and contextual relationships between words.

5. Inference

During inference, LLMs generate text token by token:

  • Input: Tokenized prompt.
  • Forward pass: Compute probabilities for the next token.
  • Decoding strategies:
  • Greedy Search: Select the token with the highest probability.
  • Beam Search: Consider multiple candidate sequences.
  • Sampling (Top-k / Top-p): Introduce randomness for more creative outputs.

6. Scaling Laws

LLMs improve with scale:

  • Parameters: More parameters allow capturing more complex patterns.
  • Data: Larger datasets improve generalization.
  • Compute: More compute enables training deeper and wider models.

Research shows that performance improves predictably with increases in model size, dataset size, and training compute.

7. Limitations

Despite their capabilities, LLMs have limitations:

  • Hallucinations: Generating plausible but incorrect information.
  • Biases: Reflecting biases present in training data.
  • Resource Intensive: Require huge computational resources for training and inference.
  • Context Window: Limited input length that the model can attend to at once.

8. Applications

LLMs are used in:

  • Text generation and summarization
  • Question answering systems
  • Chatbots and virtual assistants
  • Code generation
  • Translation and multilingual NLP
  • Sentiment analysis and classification

9. Future Directions

  • Multimodal Models: Combining text, images, audio, and video.
  • Efficient Training: Reducing compute and memory requirements.
  • Better Alignment: Improving safety and alignment with human values.
  • Continual Learning: Adapting to new data without full retraining.

Large Language Models are transforming how machines understand and generate language. Their capabilities continue to grow with scale, improved architectures, and better training strategies, but careful handling is necessary to mitigate risks and biases.

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