Semantic Delta: An Interpretable Signal Differentiating Human and LLMs Dialogue
This work provides a zero-shot, computationally inexpensive signal for detecting AI-generated text, contributing incrementally to ensemble detection systems and empirical understanding of LLM behavioral mimicry.
The authors tackled the problem of distinguishing human-written from LLM-generated dialogue by introducing a lightweight, interpretable metric called semantic delta, which measures the difference in intensity between the two most dominant semantic categories; results showed that AI-generated texts consistently had higher deltas, indicating more rigid topic structures compared to human dialogue.
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.