Large Language Model (LLM)
A Large Language Model (LLM) is a type of artificial intelligence model trained on vast amounts of text data to understand and generate human-like text. These models have billions or trillions of parameters and can perform a wide range of language tasks.
Key Characteristics
- Scale: LLMs typically have billions or trillions of parameters
- Training Data: Trained on massive datasets of text from the internet, books, articles, and other sources
- Architecture: Usually based on transformer architectures with attention mechanisms
- Capabilities: Can generate text, answer questions, summarize content, translate languages, write code, and more
Examples of LLMs
- GPT-4 (OpenAI)
- Claude (Anthropic)
- Llama 2 (Meta)
- Mistral (Mistral AI)
- PaLM (Google)
- Gemini (Google)
Limitations
- Knowledge Cutoff: LLMs only have knowledge up to their training cutoff date
- Hallucinations: May generate plausible-sounding but incorrect information
- Context Window: Limited by the amount of text they can process at once
- Bias: May reflect biases present in their training data