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highly recommended generative technique: whenever you're selecting an item at random where the items are weighted (e.g. by their frequency in a corpus), normalize the weights and sample it with a softmax function, using temperature as a parameter. (at temp=1.0, it's the same as picking by the weights directly; at <1.0 it favors items that are weighted more heavily; as temperature increases >1.0, the sampling approaches a uniform distribution)

here are examples from the thing I'm working on

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(in this case, I'm replacing phrases in sentence templates that match the part of speech of the phrase being replaced in the sentence. the effect of the temperature change is a little subtle because the replacement operation only takes place about half the time, but you can see with the low temperature example you get the same high-probability items over and over again and with the high temperature example you get a lot of weird stuff)

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