LLM Architecture
Introduction
Large Language Models (LLMs) are built upon sophisticated neural network architectures that have evolved dramatically in recent years. This document explores the core architectural components and innovations that power today's most advanced language models.
Transformer Architecture
The foundation of modern LLMs is the Transformer architecture, introduced in the landmark 2017 paper "Attention Is All You Need" by Vaswani et al.
Key Components
Self-Attention Mechanism
Allows the model to weigh the importance of different words in a sentence
Captures long-range dependencies that were difficult for previous architectures
Enables parallel processing of sequence data
Multi-Head Attention
Splits attention into multiple "heads" that can focus on different aspects of the input
Enhances the model's ability to capture various types of relationships in the data
Feed-Forward Networks
Two-layer neural networks applied to each position independently
Add non-linearity and increase the model's representational power
Layer Normalization and Residual Connections
Stabilize and accelerate training
Allow for building very deep networks without gradient problems
Architectural Variations
Decoder-Only Models
GPT family, LLaMA, Mistral
Trained to predict the next token in a sequence
Well-suited for generative tasks
Encoder-Only Models
BERT, RoBERTa
Bidirectional context processing
Effective for classification and understanding tasks
Encoder-Decoder Models
T5, BART
Combined architecture for sequence-to-sequence tasks
Used for translation, summarization, etc.
Scaling Trends
LLM architecture development has been characterized by a "scaling race" with models growing from:
BERT (340M parameters)
GPT-3 (175B parameters)
PaLM (540B parameters)
GPT-4 (estimated trillions of parameters)
Recent Innovations
Mixture of Experts (MoE)
Activates only a subset of the network for each input
Allows for much larger models with similar computational costs
Examples: Mixtral, Switch Transformers
State Space Models
Mamba, Hyena
Alternative to attention that scales linearly with sequence length
Promising for handling very long contexts
Future Directions
More efficient attention mechanisms
Sparse activation patterns
Hardware-aware architecture design
Modular architectures with specialized components
References
Vaswani, A., et al. (2017). Attention Is All You Need. https://arxiv.org/abs/1706.03762
Brown, T., et al. (2020). Language Models are Few-Shot Learners. https://arxiv.org/abs/2005.14165
Chowdhery, A., et al. (2022). PaLM: Scaling Language Modeling with Pathways. https://arxiv.org/abs/2204.02311
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