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Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9
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=== Sometimes the model isn't sure what to output next: === Examples: “Then he picked up the…” “Next, she opened the…” The next word can be many things, and the probability distribution is not steep — it’s wide and shallow. In these situations: * Top-k = 50 always keeps 50 tokens, even if the bottom 40 of them are nonsense. * Temperature lowers randomness, but nonsense tokens are still present. Top-p fixes this. Top-p dynamically chooses the number of tokens, depending on how confident the model is. * If the model is confident → very few tokens survive. * If the model is confused → more tokens survive, but still only the ones that matter. This makes the output more natural, less unpredictable, and less stupid.
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