Spot the error in this explanation of temperature.

**Temperature** is a setting that controls how a language model samples its next token. The model produces a vector of raw scores called **logits** over the vocabulary, one score per possible next token. Softmax turns those into probabilities and the sampler picks one. Temperature changes the softmax. The logits are divided by T before softmax. When T is small (close to 0), dividing makes the gaps between logits much larger, so the highest one dominates and the model becomes nearly deterministic, picking the same top token every time. At T=0 you get pure argmax (greedy decoding). When T is large (above 1), the gaps shrink and the probabilities flatten out. Less-likely tokens get a larger share of the probability mass, so the output becomes more random and creative. The passage gets all three claims backwards. T=0 is deterministic, not creative. T=1 leaves the distribution unchanged, not deterministic. Low T is for precision tasks like math and code, not unpredictability.

**Temperature** rescales the logits before softmax. The transformed distribution is: $$P(x_i) = \frac{e^{z_i / T}}{\sum_j e^{z_j / T}}$$ Where `z_i` are the raw logits. The direction is the opposite of what the passage states. **T less than 1** divides logits by a small number, amplifying differences. The softmax concentrates near argmax. At T=0 (limit), the sampler always picks the highest-logit token. This is greedy decoding. It is fully deterministic. **T = 1** changes nothing: you sample from the model's own distribution. **T greater than 1** flattens the distribution toward uniform. Sampling becomes more diverse and surprising, at the cost of coherence. The passage gets all three wrong. It labels T=0 as 'diverse and creative' (wrong, T=0 is greedy). It labels T=1 as 'deterministic' (wrong, T=1 is unchanged). It claims low T is for unpredictable outputs (wrong, low T is for precision tasks like code generation and factual question answering).