Syntactic complexity convergence in dialogue: Analysis of the phenomenon and application to LLM detection Wang, Yu Chen, Yanran Wang, Yifan Eger, Steffen Buschmeier, Hendrik We revisit the under-explored phenomenon of syntactic complexity convergence in dialogue. Extending prior topic-level analyses to full conversations, we examine five human-human datasets and a set of human-LLM dialogues. We integrate and evaluate a multidimensional set of syntactic complexity metrics to quantify structural variation in dialogue. Our analyses reveal robust syntactic complexity convergence across human-human datasets, especially in task-oriented interactions, and statistical tests confirm that these effects are not due to chance. In contrast, human-LLM dialogues show no significant convergence under individual metrics; however, linear-CKA uncovers a distinct cross-metric convergence pattern. Motivated by these differences, we use syntactic complexity as a feature for detecting LLM-involved dialogue, achieving 96\% accuracy. Our classification results demonstrate its potential without relying on likelihood-based signals such as surprisal, which require a language model as the estimator. ACL 2026 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://ris.uni-paderborn.de/record/66885 Wang Y, Chen Y, Wang Y, Eger S, Buschmeier H. Syntactic complexity convergence in dialogue: Analysis of the phenomenon and application to LLM detection. In: <i>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</i>. ACL. eng info:eu-repo/semantics/closedAccess