Temperature Sampling is Entropy-Optimal: An Information-Theoretic Framework for LLM Decoding
Jin Sima, Nikolaos Papagiannis, Ananth Grama et al.
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This work proposes a novel and practical framework for dependence testing in labeled graphs via mutual information over a structure-weighted joint label distribution and demonstrates that the proposed test is a statistically sound and an effective tool for uncovering nontrivial dependencies in graph data.