Comment by suprjami
2 days ago
Temperature, top-up, top-k, min-p all control which token the model predicts next and how likely it is to select one token over the other.
You might understand this as "The capital of France is..." and the model isn't always going to select "Paris". Sometimes it will start a descriptive sentence or even get the answer wrong.
That selection of the next token is what these settings control, and lots of sub-optimal selections compound over time to produce a junk response.
I broadly knew that about temperature, but lack the background in machine learning/statistics to differentiate top-n-sigma from top-k/top-p.
Top-K: example setting 20. Select only from the 20 most likely tokens.
Top-P: example setting 0.9. Select tokens whose probably accumulates to this number. So say you have tokens with 0.7 then 0.2 then 0.1, the last will not be selected because the first two tokens already accumulated to >=0.9.
Min-P: example setting 0.05. Don't select tokens less probable than this value. So a token with 0.1 would be considered, a token with 0.01 would not.
The purpose of all of these is to exclude very unlikely next tokens.
Min-p is specifically "Don't select tokens less probable than a multiple of the top token's probability" with min_p of 0.1 multiplied by an example top probability of 0.3 being 0.03 as the truncation at that time step.
Source: One of the min_p authors
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So do I, but we live in the future: https://chatgpt.com/share/6a7fc3d2-39f4-83e8-a7c6-825ddfb5e7...
You might find this article relevant: https://news.ycombinator.com/item?id=49151933
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