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. 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: $$P(x_i) = \frac{e^{z_i / T}}{\sum_j e^{z_j / T}}$$ Here `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).