LLM Temperature: Equation and Effect

Question: What is temperature in LLMs? Give the equation and explain its effect on outputs. Key points: Temperature scales the logits before softmax: p_i = exp(z_i / T) / sum_j exp(z_j / T). Lower T (< 1) sharpens the distribution toward the highest-probability tokens, making output more deterministic/repetitive; higher T (> 1) flattens the distribution, increasing randomness/diversity (and risk of incoherence). T=1 leaves the raw softmax distribution unchanged.