Definition
A large language model (LLM) is a neural network trained on vast text corpora to understand and generate human language. LLMs use billions of parameters to capture linguistic patterns.
Purpose
The purpose is to enable advanced NLP tasks such as text generation, summarization, and translation. LLMs are used in chatbots, search, and productivity tools.
Importance
- Powers modern conversational AI.
- Risk of bias, misinformation, and hallucinations.
- High computational and environmental costs.
- Requires careful alignment and governance.
How It Works
- Collect large-scale text datasets.
- Tokenize text into numerical representations.
- Train transformer models with billions of parameters.
- Learn to predict the next token in context.
- Fine-tune or adapt to downstream tasks.
Examples (Real World)
- GPT-4 (OpenAI): used in ChatGPT.
- PaLM (Google): large-scale LLM for research and products.
- LLaMA (Meta): open research-focused LLM.
References / Further Reading
- Vaswani et al. “Attention Is All You Need.” NeurIPS 2017.
- OpenAI GPT-4 System Card.
- Stanford CRFM. “Foundation Models.”