Sampling, Temperature, and Top-p

A language model typically assigns scores to possible next tokens. A decoding procedure turns those scores into an actual choice, then repeats the process with the new token included. This is why the same model and prompt can produce different responses. Sampling settings influence which possibilities are explored; they do not change the facts available to the model or directly measure the educational quality of an answer.

Temperature changes how concentrated the next-token probability distribution becomes. Lower positive temperatures emphasize higher-scoring tokens; higher temperatures spread probability more broadly. Temperature is therefore a control over sampling behavior, not an intelligence or creativity meter. A model can repeat a misconception consistently at a low temperature. A higher setting can produce useful variety, but also irrelevant or less coherent continuations. Some interfaces treat zero as a request for greedy selection, although exact behavior depends on the implementation.

Top-p, also called nucleus sampling, uses a different rule. It retains a smallest high-probability set whose cumulative probability reaches a selected threshold, then samples within that set. Suppose four possible continuations have probabilities 0.50, 0.30, 0.15, and 0.05. With a top-p threshold of 0.80, the first two form the candidate set in this simplified example. Their probabilities are renormalized before sampling. The number of retained candidates changes as the distribution changes, unlike a fixed top-k limit.

A teacher creating discussion starters can investigate these settings without assuming that one value is universally best. Keep the model, prompt, and source passage fixed. Request several questions under each of two temperature settings, while holding other supported controls constant. Then judge the questions against the same rubric: curricular relevance, factual assumptions, accessibility, and whether they invite different defensible responses. Count near-duplicates as well as obviously weak questions. The purpose is to observe a tradeoff in this task, not to crown a setting after one attractive output.

For a factual classroom explanation, source quality and verification usually matter more than stylistic variety. Asking for lower temperature does not prevent fabricated references or incorrect arithmetic. Constraining the response to an approved passage, checking each claim, and validating calculations address different failure points. If an activity needs reproducible examples, record the model version, prompt, available decoding settings, and generated material. A random seed may help within a particular system, but it is not a promise of identical results across software, hardware, or model changes.

Changing several sampling controls together makes a comparison difficult to interpret. Begin with a documented default and change one relevant control at a time. Some models or hosted services do not expose these controls, and settings with the same name can interact differently across implementations. For learners, the valuable insight is that fluent text emerges through a sequence of constrained choices. Sampling shapes variation within those choices; educators still have to decide whether the resulting content is accurate, inclusive, and useful for the intended learning goal.