Large Language Models (LLMs) are a class of neural networks designed to understand and generate human-like text. Built on the Transformer architecture, they’re trained on massive corpora and have revolutionized natural language processing.
1. Transformer Foundations
- Self-Attention lets the model weigh the importance of different words when encoding a sentence.
- Multi-Head Attention captures diverse relationships in parallel.
- Positional Encodings give the model a sense of word order.
2. Training at Scale
- Trained on trillions of tokens (web pages, books, code).
- Pretraining phase: predict masked or next tokens in text.
- Fine-tuning: adapt the pretrained model to specific tasks (e.g., sentiment analysis, question answering).
3. Capabilities
- Text Generation: drafting emails, writing code, composing poetry.
- Completion & Summarization: finishing partial text, condensing long documents.
- Translation & Paraphrasing.
4. Leading Models
- GPT-4 series by OpenAI
- LLaMA family by Meta AI
- Claude by Anthropic
- Gemini by Google DeepMind
5. Considerations
- Compute & Cost: Training and inference require significant GPU resources.
- Bias & Safety: Models can reflect biases present in training data—mitigation is an active research area.
- Prompt Engineering: Crafting the right inputs to elicit desired outputs.
LLMs are at the forefront of AI research and productization. Upcoming posts will explore prompt design, fine-tuning workflows, and advanced use-cases like retrieval-augmented generation.