Abstract
Token sampling strategies critically influence text generation quality in large language models (LLMs). However, existing methods introduce additional hyperparameters, requiring extensive tuning and complicating deployment. We present Entropy Equilibrium Sampling (EES), an auxiliary hyperparameter-free approach inspired by information theory that can dynamically adjust candidate sets by balancing normalized entropy with probability mass. We evaluate EES on both reasoning and generation tasks across a range of model architectures. Our results show that EES consistently performs well across temperature settings, delivering competitive accuracy and coherence while maintaining diversity. By eliminating the need for hyperparameter tuning, EES greatly simplifies deployment while improving performance. Code is available at this https URL
Abstract (translated)
令牌采样策略对大规模语言模型（LLMs）的文本生成质量有关键影响。然而，现有的方法引入了额外的超参数，需要进行大量的调优，并且增加了部署的复杂性。我们提出了熵均衡采样（Entropy Equilibrium Sampling, EES），这是一种受信息理论启发的辅助无超参数方法，能够通过平衡归一化熵和概率质量来动态调整候选集。我们在多种模型架构上对EES进行了推理任务和生成任务上的评估。我们的结果显示，在不同温度设置下，EES始终表现良好，提供了具有竞争力的准确性和连贯性，同时保持了多样性。通过消除超参数调优的需求，EES大大简化了部署过程，并提高了性能。 代码可在 [提供的URL] 获取。
URL
https://arxiv.org/abs/2512.00789
