AI / LLM Basics Interview Questions
Describe the role of Softmax in an LLM's output layer?
Softmax is the function that converts the model's raw output scores for every possible next token into a proper probability distribution that sums to one.
- Takes a raw score for every token in the vocabulary, some tens of thousands of numbers
- Converts those scores into probabilities, with higher raw scores becoming higher probabilities
- The model then samples, or picks, its next token based on this probability distribution
This is the final step that turns the model's internal computation into an actual choice of which word comes next, and it's also the point where settings like temperature come into play.
What does softmax convert raw output scores into?
What setting comes into play around the same point as softmax?
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