Artificial intelligence is developing quickly, and large language models (LLMs) and generative AI are now common topics. What is an LLM, and how does generative AI work? This guide introduces the ideas behind tools such as ChatGPT, Claude, and Gemini.
What is a large language model?
A large language model, or LLM, is designed to process and generate language. LLMs are behind many familiar AI tools, including ChatGPT and Claude.
What is generative AI?
Generative AI can create new material, such as text, images, or audio. It receives input and produces an output in response to that input or instruction.
Neural networks and deep learning
Neural networks are a foundation of deep learning and many LLMs. They use connected nodes arranged in layers to process information, in a simplified design inspired by how the brain is described. Deep learning is a branch of machine learning that uses neural networks with multiple layers to learn from data.
Transformers: an architecture used by LLMs
The Transformer is an architecture that changed natural language processing (NLP). It helps language models process relationships within text and generate language.
Encoders, decoders, and vectors
An encoder processes input and represents information as numerical vectors. A decoder uses those representations to produce an output, such as text. Vectors allow a model to perform calculations on information in a numerical form.
Attention and position
Attention helps a Transformer calculate how different parts of the input relate to one another, so the model can focus on relevant information. Positional information helps it account for the order of words or other elements in a sequence.
How LLMs work and are trained
Many LLMs use Transformer architecture. During training, a model learns patterns from large collections of data. The source article mentions masking and next-sequence prediction as examples of training techniques. After training, a model processes a prompt and generates a text response.
Pretrained models
A pretrained model has already learned from a training dataset. It can be used for general tasks or fine-tuned with additional training for a more specific purpose.
Unsupervised and supervised learning
In unsupervised learning, data is used without labels supplied for each example; the model learns patterns in that data. In supervised learning, examples include labels that guide the model toward a defined task.
Instructions, prompts, and in-context learning
An instruction-tuned or instruct model is further trained or adjusted to respond more effectively to instructions. A prompt is the input or request given to an LLM. Prompt engineering means designing and refining prompts to guide the model toward a useful response.
In-context learning gives the model examples alongside a prompt. Those examples can guide its response without retraining the model for each request.
Reinforcement learning and fine-tuning
Reinforcement learning uses actions and feedback, often described as rewards or penalties, to improve a model’s behavior. In the simplified example in the source article, a model takes an action, receives a reward, and learns to favor actions associated with higher rewards. Deep reinforcement learning combines deep learning with reinforcement learning.
Examples and tools
Hugging Face provides access to language models and related tools. GPT-3, Gemini, and Llama are examples of well-known language models. The architecture, training data, and capabilities differ by model and version.
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