I couldn't disagree more, turning temp to zero is like taking a monte carlo method and only using one sample, or a particle filter with only one particle. Takes

I couldn't disagree more, turning temp to zero is like taking a monte carlo method and only using one sample, or a particle filter with only one particle. Takes the entire concept and throws it out of the window so you can have predictability. LLMs need to probabilistically explore the generation domain to converge on a good result for best performance. Similar issue with people benchmarking models by only having them output one single token (e.g. yes or no) outright, which prevents any real computation from occurring so the results are predictably poor.