---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Yu
      foaf_name: Wang, Yu
      foaf_surname: Wang
  - foaf_Person:
      foaf_givenName: Yanran
      foaf_name: Chen, Yanran
      foaf_surname: Chen
  - foaf_Person:
      foaf_givenName: Yifan
      foaf_name: Wang, Yifan
      foaf_surname: Wang
  - foaf_Person:
      foaf_givenName: Steffen
      foaf_name: Eger, Steffen
      foaf_surname: Eger
  - foaf_Person:
      foaf_givenName: Hendrik
      foaf_name: Buschmeier, Hendrik
      foaf_surname: Buschmeier
      foaf_workInfoHomepage: http://www.librecat.org/personId=76456
    orcid: 0000-0002-9613-5713
  dct_date: 2026^xs_gYear
  dct_language: eng
  dct_publisher: ACL@
  dct_title: 'Syntactic complexity convergence in dialogue: Analysis of the phenomenon
    and application to LLM detection@'
...