@inproceedings{wang-etal-2026-syntactic,
title = "Syntactic Complexity Convergence in Dialogue: Analysis of the Phenomenon and Application to {LLM} Detection",
author = "Wang, Yu and
Chen, Yanran and
Wang, Yifan and
Eger, Steffen and
Buschmeier, Hendrik",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1239/",
pages = "28244--28269",
ISBN = "979-8-89176-459-0",
abstract = "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."
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%0 Conference Proceedings
%T Syntactic Complexity Convergence in Dialogue: Analysis of the Phenomenon and Application to LLM Detection
%A Wang, Yu
%A Chen, Yanran
%A Wang, Yifan
%A Eger, Steffen
%A Buschmeier, Hendrik
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-syntactic
%X 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.
%U https://aclanthology.org/2026.emnlp-main.1239/
%P 28244-28269
Markdown (Informal)
[Syntactic Complexity Convergence in Dialogue: Analysis of the Phenomenon and Application to LLM Detection](https://aclanthology.org/2026.emnlp-main.1239/) (Wang et al., EMNLP 2026)
ACL