Abstract
Standard decoding strategies for text generation, including top-k, nucleus sampling, and contrastive search, select tokens based on likelihood, restricting selection to high-probability regions. Human language production operates differently: tokens are chosen for communicative appropriateness rather than statistical frequency. This mismatch creates a truncation blind spot: contextually appropriate but statistically rare tokens remain accessible to humans yet unreachable by likelihood-based decoding. We hypothesize this contributes to the detectability of machine-generated text. Analyzing over 1.8 million texts across eight language models, five decoding strategies, and 53 hyperparameter configurations, we find that 8-18% of human-selected tokens fall outside typical truncation boundaries. Simple classifiers trained on predictability and lexical diversity achieve remarkable detection rates. Crucially, neither model scale nor architecture correlates strongly with detectability; truncation parameters account for most variance. Configurations achieving low detectability often produce incoherent text, indicating that evading detection and producing natural text are distinct objectives. These findings suggest detectability is enhanced by likelihood-based token selection, not merely a matter of model capability.
Abstract (translated)
文本生成的标准解码策略（包括top-k采样、核心采样和对比搜索）均基于概率选择词元，并将选择范围限制在高概率区域。而人类语言产出的运作方式不同：词元的选择基于交际恰当性而非统计频率。这种错配造成了一个“截断盲区”：那些在语境中恰当但统计上罕见的词元，人类可以自然使用，却无法被基于概率的解码策略所触及。我们假设这加剧了机器生成文本的可检测性。通过分析涵盖八个语言模型、五种解码策略及五十三种超参数配置的超过180万条文本，我们发现8%至18%的人类选词超出了常规截断边界。基于可预测性和词汇多样性训练的简单分类器实现了显著的检测率。关键在于，模型规模或架构与可检测性并无强相关性，截断参数解释了大部分方差。那些实现低检测率的配置往往产生不连贯的文本，这表明规避检测与生成自然文本是两个截然不同的目标。这些发现表明，基于概率的词元选择增强了文本的可检测性，这远非单纯模型能力的问题。
URL
https://arxiv.org/abs/2603.18482
