💬 Large Language Models

The text a language model produces after a prompt, built token by token from patterns learned during training.

The text, images or other inputs a model can currently use to shape its next output in a session.

The maximum amount of input and prior output a language model can use at one time while producing the next part of a response.

A generated answer that sounds plausible but includes false, unsupported or invented details.

A language model estimates probabilities for pieces of language in context; many can use these estimates to generate text from a prompt.

A very large text-processing model predicts likely next pieces of language from a prompt and uses that pattern-matching ability to generate or transform text.

The delay between sending a request to a model and receiving its reply, or the next part of that reply.

A language model produces text by repeatedly estimating probabilities for small pieces of text that could come next in the current sequence.

A parameter is a learned numerical setting inside a model that helps shape which token it is likely to produce next.

The text, image or other input that guides a model on what kind of output to produce next.

The practice of writing and refining instructions so a language model is more likely to produce a useful, safe and well-structured reply.

A token is one unit from a model’s vocabulary used to represent input or output, often smaller than a word.

The input a person gives a chat assistant to guide what kind of response it should produce.

In language models, apparent comprehension usually means producing text that fits patterns in data and context, not reliably building a grounded grasp of the world like a person does.

The same full input and generation conditions produce the same output every time, rather than varying across repeated runs.

A released file of learned numerical settings that lets compatible software run a trained model without repeating the original training process.

A multimodal model can take in or produce more than one kind of data, such as text, images or audio, within one system.

A reasoning model is a language model designed for stronger performance on multi-step tasks, often using extra computation while answering, though visible steps may not reflect its internal process.

A stochastic procedure that draws one next token from the model’s probability distribution, appends it to the output, and repeats until a stop condition.

A high-priority instruction that sets the assistant’s role, rules and priorities before it responds to later messages.

A setting that controls how predictable or varied a language model’s next chosen words are during generation.

Throughput measures how much text a model can process or generate in a given time, usually counted in tokens per second.

The process of breaking text into smaller units a language model can process, such as words, parts of words, punctuation marks or special symbols.

A decoding setting that limits each next-token choice to the smallest group whose combined probability reaches a chosen threshold, such as 0.9.

A VLM links images and text so one model can take visual input, written input, or both, and produce text or other structured outputs about them.

The full set of tokens a language model can represent and choose from during text processing and generation.