Plain-language explainer
Softmax, explained
What does softmax do, and where does an LLM use it?
Softmax turns a list of raw scores into percentages that add up to 100. It exaggerates gaps: a score slightly ahead becomes a share far ahead, and low scores shrink toward zero without ever quite reaching it. Language models use it in the two places that matter most: at the output, to turn word scores into the next-word probabilities you sample from, and inside attention, to decide how much each earlier word contributes to the current one.
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What people get wrong
- Softmax picks the winner. It only converts scores into shares. The picking, sampling or taking the top one, happens after.
- The percentages are the model's confidence about facts. They describe likely text, not truth, which is why a wrong answer can carry a high share.
- It is an obscure detail. The temperature dial you see in products works by reshaping scores right before this exact step.
Where you see it in real products
- APIs that return logprobs are showing you the shares softmax produced.
- The temperature setting is arithmetic applied just before softmax.
- Attention heatmaps in AI visualizations are softmax shares drawn as color.
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