Model Components
Language Model
A language model learns a probability distribution over word sequences from large text corpora. Given a prefix of text, it predicts the probability of each possible next word (or token). Autoregressive generation samples from these distributions to produce fluent text.
Language models range from simple n-gram models to complex transformer LLMs with billions of parameters. They are the core component of machine translation, speech recognition, text generation, and AI search systems.
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Related Terms
Model Components
Large Language Model (LLM)
A transformer-based neural network with billions to trillions of parameters, trained on broad text corpora to predict the next token and able to generate, summarize, classify, and reason over natural language.
Techniques & Methods
Autoregression
Statistical modeling approach where future values are predicted from past observed values.
Techniques & Methods
Pre-training
Initial phase where a model learns general representations from large datasets before task-specific fine-tuning.
Core Concepts
Natural Language Processing (NLP)
Field focused on enabling computer-human interaction through natural language.