Large Language Models (LLMs)
AI models that use transformer architecture to understand and generate human-like text by predicting the next token in a sequence.
Also known as: LLM, LLMs, Language Model, Foundation Model
Category: AI
Tags: ai, machine-learning, nlp, transformers, deep-learning, text-generation
Explanation
Large Language Models (LLMs) are AI systems trained on massive text datasets to generate human-like text. They work by predicting the most probable next word (token) based on the preceding context, using a transformer architecture with attention mechanisms.
LLMs use induction rather than deduction - they make highly-educated statistical guesses based on patterns learned during training. This explains why techniques like Chain-of-Thought prompting are effective: making the model 'think out loud' produces more correct-sounding word sequences.
Key components include:
- **Encoders**: Convert tokens into numerical representations (embeddings) that capture semantic meaning
- **Decoders**: Generate output by predicting the next token based on context
- **Attention mechanism**: Weighs the importance of surrounding words to determine meaning in context
- **Context window**: The number of tokens the model can process at once (e.g., 128K for GPT-4, 200K for Claude)
LLMs are trained using Reinforcement Learning from Human Feedback (RLHF) to align outputs with human preferences. They can be extended with Retrieval Augmented Generation (RAG) to access external, up-to-date information.
The key to effective LLM usage is treating them as evolution engines - generating initial attempts and iteratively improving through guided feedback.
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