做文本生成或LLM解码优化的开发者，这篇论文直接点出了采样参数对语言多样性的隐性伤害，建议用WCS指标检查自己的模型输出是否过于单调。
论文提出Word Coverage Score (WCS)指标，量化标准采样过滤器（Top-p、Top-k、Min-p）对低频高信息词汇的抑制程度。研究发现，行业默认采样参数会系统性剪除人类文本中独特表达，导致模型输出同质化。WCS为平衡文本连贯性与词汇丰富度提供了诊断工具，帮助开发者优化解码策略。
Lost in Sampling: Assessing Lexical Reachability in LLMs via the Word Coverage Score (WCS)
Modern Large Language Models (LLMs) are often criticized for producing repetitive and homogeneous text, despite possessing vast latent vocabularies. While previous research has focused on model knowledge and training data, we investigate the role of decoding mechanics in suppressing linguistic diversity. We introduce the Word Coverage Score (WCS), a metric that quantifies the extent to which contextually appropriate human vocabulary is mathematically pruned by standard sampling filters (e.g., Top-$p$, Top-$k$, and Min-$p$). Rather than assessing static knowledge, the WCS measures the lexical survival rate of low-frequency, high-information human words as a function of sampling parameters. By auditing open-weight models on human-authored corpus fragments, we identify which logical lexical choices are rendered unreachable by the decoder, even when they reside within the probability space. Our results provide quantitative evidence that industry-standard sampling defaults act as unintended censorship mechanisms, smoothing the unique textures of human expression into a homogenized discourse. The WCS offers a rigorous framework for optimizing the trade-off between text coherence and lexical richness, providing a diagnostic tool for preserving the diversity of human language in generative models.