[{"date_updated":"2026-08-31T17:33:32Z","publication_status":"accepted","status":"public","title":"Syntactic complexity convergence in dialogue: Analysis of the phenomenon and application to LLM detection","year":"2026","conference":{"end_date":"2026-10-29","start_date":"2026-10-24","name":"2026 Conference on Empirical Methods in Natural Language Processing","location":"Budapest, Hungary"},"author":[{"full_name":"Wang, Yu","last_name":"Wang","first_name":"Yu"},{"first_name":"Yanran","last_name":"Chen","full_name":"Chen, Yanran"},{"full_name":"Wang, Yifan","last_name":"Wang","first_name":"Yifan"},{"first_name":"Steffen","last_name":"Eger","full_name":"Eger, Steffen"},{"id":"76456","full_name":"Buschmeier, Hendrik","orcid":"0000-0002-9613-5713","last_name":"Buschmeier","first_name":"Hendrik"}],"user_id":"76456","language":[{"iso":"eng"}],"_id":"66885","publisher":"ACL","abstract":[{"text":"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.","lang":"eng"}],"quality_controlled":"1","project":[{"name":"TRR 318-2 - Project B07: Communicative practices of requesting information and explanation from LLM-based agents","_id":"2752"},{"name":"TRR 318-2 - Project A02: Monitoring the understanding of explanations","_id":"2736"}],"publication":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","citation":{"bibtex":"@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} }","ama":"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.","mla":"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.","chicago":"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.","short":"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.","ieee":"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.","apa":"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."},"type":"conference","department":[{"_id":"660"}],"place":"Budapest, Hungary","date_created":"2026-08-31T17:27:49Z"}]