Do LLMs talk like us? This question intrigues a multitude of scholar and it is relevant in many fields, from education to academia. This work presents an interpretable statistical feature for distinguishing human written and LLMs generated dialogue. We introduce a lightweight metric derived from semantic categories distribution. Using the Empath lexical analysis framework, each text is mapped to a set of thematic intensity scores. We define semantic delta as the difference between the two most dominant category intensities within a dialogue, hypothesizing that LLM outputs exhibit stronger thematic concentration than human discourse. To evaluate this hypothesis, conversational data were generated from multiple LLM configurations and compared against heterogeneous human corpora, including scripted dialogue, literary works, and online discussions. A Welch t-test was applied to the resulting distributions of semantic delta values. Results show that AI-generated texts consistently produce higher deltas than human texts, indicating a more rigid topics structure, whereas human dialogue displays a broader and more balanced semantic spread. Rather than replacing existing detection techniques, the proposed zero-shot metric provides a computationally inexpensive complementary signal that can be integrated into ensemble detection systems. These finding also contribute to the broader empirical understanding of LLM behavioural mimicry and suggest that thematic distribution constitutes a quantifiable dimension along which current models fall short of human conversational dynamics.

翻译：大型语言模型（LLM）的对话方式是否与人类相似？这一问题引发了众多学者的兴趣，并在从教育到学术的多个领域具有关联性。本文提出一种可解释的统计特征，用于区分人类撰写的对话与LLM生成的对话。我们引入了一种基于语义类别分布的轻量级度量方法。借助Empath词汇分析框架，将每个文本映射为一组主题强度评分。我们将语义差异定义为对话中两个主导类别强度之差，假设LLM的输出比人类话语表现出更强的主题集中性。为验证这一假设，我们从多种LLM配置中生成了对话数据，并与异质性人类语料库（包括剧本对话、文学作品和在线讨论）进行了比较。对语义差异值的分布应用了韦尔奇t检验。结果表明，AI生成的文本始终比人类文本产生更高的语义差异值，表明其主题结构更刻板，而人类对话则展现出更广泛、更均衡的语义分布。所提出的零样本度量并非取代现有检测技术，而是提供一种计算成本低廉的互补信号，可集成到集成检测系统中。这些发现也有助于从实证角度更广泛地理解LLM的行为模仿特征，并表明主题分布构成了当前模型在人类对话动态方面仍有不足的一个可量化维度。