conference paper accepted yes Yu Wang author Yanran Chen author Yifan Wang author Steffen Eger author Hendrik Buschmeier author 764560000-0002-9613-5713 660 department 2026 Conference on Empirical Methods in Natural Language Processing TRR 318-2 - Project B07: Communicative practices of requesting information and explanation from LLM-based agents project TRR 318-2 - Project A02: Monitoring the understanding of explanations project 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. ACL2026Budapest, Hungary eng Wang, Yu, Yanran Chen, Yifan Wang, Steffen Eger, and Hendrik Buschmeier. “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>. Budapest, Hungary: ACL, n.d. Y. Wang, Y. Chen, Y. Wang, S. Eger, H. Buschmeier, in: Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, ACL, Budapest, Hungary, n.d. Y. Wang, Y. Chen, Y. Wang, S. Eger, and H. Buschmeier, “Syntactic complexity convergence in dialogue: Analysis of the phenomenon and application to LLM detection,” presented at the 2026 Conference on Empirical Methods in Natural Language Processing, Budapest, Hungary. Wang, Y., Chen, Y., Wang, Y., Eger, S., &#38; Buschmeier, H. (n.d.). Syntactic complexity convergence in dialogue: Analysis of the phenomenon and application to LLM detection. <i>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</i>. 2026 Conference on Empirical Methods in Natural Language Processing, Budapest, Hungary. @inproceedings{Wang_Chen_Wang_Eger_Buschmeier, place={Budapest, Hungary}, title={Syntactic complexity convergence in dialogue: Analysis of the phenomenon and application to LLM detection}, booktitle={Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing}, publisher={ACL}, author={Wang, Yu and Chen, Yanran and Wang, Yifan and Eger, Steffen and Buschmeier, Hendrik} } 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. Wang, Yu, et al. “Syntactic Complexity Convergence in Dialogue: Analysis of the Phenomenon and Application to LLM Detection.” <i>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</i>, ACL. 668852026-08-31T17:27:49Z2026-08-31T17:33:32Z