AIBullisharXiv – CS AI · 14h ago7/10
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Min-$k$ Sampling: Decoupling Truncation from Temperature Scaling via Relative Logit Dynamics
Researchers propose Min-k Sampling, a novel decoding strategy for large language models that dynamically identifies semantic cliffs in logit distributions to optimize token truncation. Unlike temperature-sensitive methods like Top-k and Top-p, Min-k achieves temperature invariance through relative logit dynamics while maintaining superior text quality across reasoning, creative writing, and human evaluation benchmarks.