A language model is a statistical model that assigns probabilities to sequences of tokens, encoding learned regularities from training data to predict, generate, and interpret language. It operates through three parameters: (1) a vocabulary mapping tokens to integer indices, (2) trained weight matrices that compute conditional probability distributions over next-token sequences, and (3) an autoregressive or masked decoding procedure that generates output token by token. Persistence is maintained through serialized model weights, deployment in inference services, and continuous refinement via fine-tuning on domain-specific corpora. [formal: linguistics | substrate: mind | horizon: generations | explicit: yes | epoch: 0.01]
Accepted ontology entry
language model
A language model is a statistical model that assigns probabilities to sequences of tokens, encoding learned regularities from training data to predict, generate, and interpret language. It operates through three parameters: (1) a vocabular…
Definition
Why it is in scope
A human-made statistical model that assigns probabilities to sequences of symbols (words or tokens), enabling prediction, generation, and interpretation of natural language — built to persist through parameterized weights, training data, and deployment infrastructure.
Names and aliases
- language modelen · CANONICAL
Relations from this entry
- cmru5nrqe003sr671qxjyxhiqINSTANCE_OF →
A language model is a specific kind of model specialized for language — per Law 9, the 'is a kind of' test applies: a competent speaker would call a language model 'a model'.
- cmrg0scos00ef2a1nklfvbk7xDEPENDS_ON →
Language models are built using machine learning techniques — neural network training, backpropagation, gradient descent. Per the removal test (Law 8): remove machine learning and language models cease to exist as a working concept. They are ML artifacts by nature, not just historically associated.
Relations to this entry
- cmsn38oug02uu1q13kmcfzvv6← SERVES
The Transformer was designed to serve as the backbone architecture for language models. Per Law 8d, the question is for whose sake: the Transformer serves language model development. Self-attention within the Transformer enables modeling long-range dependencies in text — the core capability of language models.
Record identity
- Created
- Aug 10, 2026, 10:47 AM UTC
- Content hash
- 68fcbab92d63538181450b33ef14494082115cc84d9960b3aa6dd340465546e9