What is a large language model?
At a minimum, a language model having a very high number of parameters.
large language model explained in plain English
At a minimum, a language model having a very high number of parameters. More informally, any Transformer-based language model, such as Gemini or GPT. See Large language models (LLMs) in Machine Learning Crash Course for more information.
Example
Practitioners refer to large language model when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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- Gemini
The ecosystem comprising Google's most advanced AI.
- GPT
A family of large language models developed by OpenAI that generate human-like text.
- pre-trained model
Although this term could refer to any trained model or trained embedding vector, pre-trained model now typically refers to a trained large language model or other form of trained generative AI model.
- retrieval-augmented generation
A technique for improving the quality of large language model (LLM) output by grounding it with sources of knowledge retrieved after the model was trained.
- tokenizer
A system or algorithm that translates a sequence of input data into tokens.
- agent orchestration
The centralized management and routing of tasks across multiple sub-agents or LLM calls.
- AI slop
Output from a generative AI system that favors quantity over quality.
- Attention
A mechanism that lets a model focus on the most relevant parts of its input when producing an output, weighting what matters most in context.
- auto-regressive model
A model that infers a prediction based on its own previous predictions.
- autoencoder
A system that learns to extract the most important information from the input.