A model has several possible continuations
During ordinary autoregressive text generation, a model assigns scores to possible next tokens given the preceding context. A decoding procedure determines how one token is selected. The model then continues from the updated context.
Greedy decoding chooses the highest-scoring next token at each step. Sampling instead draws from a probability distribution. Hugging Face’s generation documentation distinguishes these approaches and other decoding methods. Temperature is one way to modify the distribution used for sampling; it is not a separate store of knowledge.
What lowering or raising temperature changes
For a positive temperature, a common implementation divides token logits by the temperature before applying softmax. A lower value makes score differences more pronounced in the resulting probabilities. A higher value makes the distribution flatter. The exact setting available to a user depends on the system.
Imagine three fictional candidate continuations with one clearly favored by the model. Lowering temperature generally makes that favored candidate more dominant. Raising it gives less-favored candidates more chance. This changes how much variation sampling can introduce without establishing which candidate is true.
The probabilities concern token selection under the model. They should not be interpreted as direct probabilities that a complete factual claim is correct.
Why temperature is not an accuracy dial
If the highest-scoring continuation contains an error, repeatedly selecting it can produce a consistent error. A lower temperature can make responses more similar without supplying missing evidence or correcting a mistaken association in the model.
Conversely, a higher temperature does not inherently make every response wrong. It changes the sampling behavior. The consequences depend on the task and available continuations. Variation may be useful for some open-ended generation tasks and undesirable for others, but that is different from a universal relationship between temperature and truth.
For a factual question, source availability, retrieval, model capability, and verification remain relevant regardless of the sampling setting. Changing how a continuation is chosen does not replace checking what supports it.
Top-k and top-p ask related but different questions
Top-k sampling limits selection to a fixed number of high-scoring candidates. Top-p, or nucleus sampling, uses a set whose cumulative probability reaches a chosen threshold. The number of candidates can therefore vary from one step to another.
Holtzman and colleagues introduced nucleus sampling in their study of text degeneration, examining how decoding affected generated text. The result concerns generation behavior under the studied conditions, not a claim that one sampling setting is optimal for every modern system or task.
| Setting | Main role |
|---|---|
| Temperature | Changes the concentration of the sampling distribution. |
| Top-k | Restricts selection to a fixed number of candidates. |
| Top-p | Restricts selection using cumulative probability mass. |
| Output limit | Restricts how much text can be generated, rather than how a token is selected. |
These settings can interact. Two systems with the same temperature may still behave differently because they use different models, filters, prompts, or decoding procedures.
Does zero mean perfectly deterministic?
Some interfaces use zero as a convention for greedy or near-greedy behavior. Mathematically, the ordinary positive-temperature expression cannot simply divide by zero; the implementation defines what that setting means.
Even when token selection is intended to be deterministic, identical visible prompts do not always guarantee identical outputs across services, versions, or execution conditions. The full system may include changing instructions, retrieval results, model updates, or numerical differences. A promise of repeatability needs to describe those conditions too.
This also explains why a random seed, where available, is not a universal guarantee across different hardware or software versions. It controls part of a process, not every possible source of variation.
Small changes can grow into different answers
Once a different token is selected, the later context changes. Subsequent choices are then conditioned on a different sequence. A small early variation can therefore lead to a substantially different response, especially in open-ended writing.
That branching does not necessarily reveal a change in the model’s beliefs. It reflects how sequential generation unfolds. Comparing complete answers is useful, but interpreting every difference as an independent reassessment of truth would overstate what the process shows.
What the setting can tell a reader
Temperature helps explain one source of output variation. It does not explain all variation, guarantee creativity, or establish reliability. The meaningful description connects the model, decoding settings, task, and evaluation.
A stable answer can still need verification, and a varied answer can still contain correct information. Keeping selection behavior separate from evidence makes the setting easier to understand without assigning it powers it does not have.
