Min-$k$ Sampling: Decoupling Truncation from Temperature Scaling via Relative Logit Dynamics

ArXi:2604.11012v1 Announce Type: new The quality of text generated by large language models depends critically on the decoding sampling strategy. While mainstream methods such as Top-$k$, Top-$p$, and Min-$p$ achieve a balance between diversity and accuracy through probability-space truncation, they share an inherent limitation: extreme sensitivity to the temperature parameter.