Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== Assistant: Ah — temperature is one of those “magical knobs” that affects the AI’s output style, and it’s actually simpler than mos… ===
Ah — temperature is one of those “magical knobs” that affects the AI’s output style, and it’s actually simpler than most people imagine once you see it in terms of probabilities. Let me break it down carefully.

==== - Temperature is a parameter that affects the randomness of token selection during generation. ====
* The model produces a probability distribution over all possible next tokens. - Example: next token probabilities might be: ``<code> token1 = 0.7   token2 = 0.2   token3 = 0.1 <syntaxhighlight>- Temperature changes how “peaked” or “flat” this probability distribution is: - Low temperature (e.g., 0.1) → makes the distribution sharper → model almost always picks the highest-probability token → deterministic, conservative outputs. - High temperature (e.g., 1.0 or 1.5) → flattens the distribution → model is more likely to pick lower-probability tokens → creative, varied, sometimes hallucinated outputs.

: 

==== - Important: temperature does not change weights, layers, or neuron connections. ====
* The internal computation of the network is identical; the model still produces the same activation outputs for each neuron.
* Temperature is applied after the neuron activations are converted into probabilities for the next token. - Specifically: the model produces logits (raw numbers for each token) → divide logits by temperature → apply softmax → sample next token.

Mathematically:

</syntaxhighlight>
P(token_i) = exp(logit_i / temperature) / sum_j(exp(logit_j / temperature))

</code>``
* Lower temperature → logits become more extreme → top token dominates
* Higher temperature → logits less extreme → more chance for alternatives

==== - Imagine neurons are baking a batch of cookies. Each cookie type has a predicted “score” (logit) for how likely it should be picked next. ====
* Temperature is like tweaking the oven: - Low temp → oven only perfectly cooks the top-scoring cookie → predictable, safe choice - High temp → oven cooks a mix of cookies, including lower-scoring ones → more variety, surprise flavors

==== - Temperature only affects output sampling, not reasoning capacity or internal calculations. ====
* Neuron activations, weights, attention patterns remain unchanged.
* Lower temperature → deterministic, precise, safer outputs
* Higher temperature → stochastic, creative, riskier outputs

If you want, I can make a diagram showing how temperature reshapes token probability distributions, and why it doesn’t touch the neurons or weights at all — it’s purely at the output sampling stage.

Do you want me to do that?