Large language model
A large language model estimates distributions over token sequences using a high-capacity neural network. Fluent continuation is not the same operation as consulting a fact database, even when outputs appear knowledgeable.
Overview. A large language model estimates distributions over token sequences using a high-capacity neural network. Fluent continuation is not the same operation as consulting a fact database, even when outputs appear knowledgeable.
Mechanism and context. A language model assigns probabilities to token sequences and produces continuations from the context it receives. Scale changes the capacity of that learned function, but the core operation remains conditional generation rather than a guaranteed lookup against a current, authoritative record.
Practical use. For an application, record the tokenizer, available context, prompt, sampling settings, and any retrieval or tool layer. These choices influence what the user sees and make a result more reproducible than a model-family name on its own.
Related reading. Read Tokenization for the model's discrete units, Prompting for inference-time conditioning, Retrieval-augmented generation for external evidence, and Language Models are Few-Shot Learners for a primary study of scale and in-context tasks.
Claims and evidence
Claim ledger, 2A large language model estimates distributions over token sequences using a high-capacity neural network.
Fluent continuation is not the same operation as consulting a fact database, even when outputs appear knowledgeable.