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From context to the next token

Separate the model’s probabilities from the rule that selects an output.

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Scientific sources
Definition

A token may be a word, part of a word or a symbol. An autoregressive language model computes scores from context; normalisation produces a distribution. Decoding then selects a token, which joins the context for the next step.

Equation

qᵢ = pᵢ^(1/T) / Σⱼ pⱼ^(1/T), T > 0

p is the reference distribution, q the transformed distribution and T a strictly positive temperature. The q values sum to 1; displayed values are rounded.

Assumptions and notation

p is the reference distribution, q the transformed distribution and T a strictly positive temperature. The q values sum to 1; displayed values are rounded.

Limits of interpretation

Probability expresses model preference in this context, not the truth of a sentence. Temperature changes sampling without adding knowledge. Our example has only three fictional possibilities.

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DefinitionEquationAssumptions and notationLimits of interpretationScientific sources
Essentials
From concept to practicePut it into practice · ChatGPT

Scientific sources

  • Vaswani et al. · 2017Attention Is All You NeedarXiv v7 · 2023
    Original publication

Teaching synthesis of the cited sources. Published results remain tied to their tasks, protocols and budgets.

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LearnLearning, then using a modelSeparate learned parameters from information supplied with a request.
LearnFrom context to the next tokenSeparate the model’s probabilities from the rule that selects an output.
LearnFrom text to representationsUnderstand why words become vectors without equating a vector with human meaning.
LearnWhat kind of memory?Separate working context, recurrent state, learned parameters and persistent storage.
LearnTransformers: understanding attentionCompare architectures, training methods and complete systems at the right level.
LearnGenerating by denoisingDistinguish corruption during learning from progressive generation.
LearnFrom a model to a system that actsUnderstand the harness around a model and its tools.
LearnPredicting to choose an actionExplore longer horizons and the risk of inaccurate predictions.
LearnDefinitions, not a single verdictExamine generality, performance and adaptation before applying a label.
LearnWhat can a result establish?Move from a convincing impression to a reproducible comparison.
ConfigurePrepare a ChatGPT projectA clear objective, useful context and instructions you can actually use.
ConfigureChoose for your taskStart with your needs. Compare access and outcomes without a universal model ranking.
ConfigureMake your request verifiableTurn an intention into an objective, context and success criteria.
ConfigurePut the right information in the right placeDistinguish working rules, task resources and memory managed by the product.
ConfigurePrepare a development environmentDefine the model’s role, harness capabilities and the access you actually need.
ConfigureCompose a development workflowOrganise preparation, changes, verification and reporting. Reuse the steps that help.
ConfigurePrepare a mission, then check the resultA reproducible example, a checklist and a record of your observations.
Public programsTentacularPublicly available uses, features and resources.