Simon Willison’s Weblog

Dummy's Guide to Modern LLM Sampling (via) This is an extremely useful, detailed set of explanations by @AlpinDale covering the various different sampling strategies used by modern LLMs. LLMs return a set of next-token probabilities for every token in their corpus - a layer above the LLM can then use sampling strategies to decide which one to use.

I finally feel like I understand the difference between Top-K and Top-P! Top-K is when you narrow down to e.g. the 20 most likely candidates for next token and then pick one of those. Top-P instead "the smallest set of words whose combined probability exceeds threshold P" - so if you set it to 0.5 you'll filter out tokens in the lower half of the probability distribution.

There are a bunch more sampling strategies in here that I'd never heard of before - Top-A, Top-N-Sigma, Epsilon-Cutoff and more.

Reading the descriptions here of Repetition Penalty and Don't Repeat Yourself made me realize that I need to be a little careful with those for some of my own uses of LLMs.

I frequently feed larger volumes of text (or code) into an LLM and ask it to output subsets of that text as direct quotes, to answer questions like "which bit of this code handles authentication tokens" or "show me direct quotes that illustrate the main themes in this conversation".

Careless use of frequency penalty strategies might go against what I'm trying to achieve with those prompts.