@inproceedings{donmez-etal-2025-ai,
title = "{AI} Argues Differently: Distinct Argumentative and Linguistic Patterns of {LLM}s in Persuasive Contexts",
author = {D{\"o}nmez, Esra and
Maurer, Maximilian and
Lapesa, Gabriella and
Falenska, Agnieszka},
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1755/",
pages = "34583--34614",
ISBN = "979-8-89176-332-6",
abstract = "Distinguishing LLM-generated text from human-written is a key challenge for safe and ethical NLP, particularly in high-stake settings such as persuasive online discourse. While recent work focuses on detection, real-world use cases also demand interpretable tools to help humans understand and distinguish LLM-generated texts. To this end, we present an analysis framework comparing human- and LLM-authored arguments using two easily-interpretable feature sets: general-purpose linguistic features (e.g., lexical richness, syntactic complexity) and domain-specific features related to argument quality (e.g., logical soundness, engagement strategies). Applied to */r/ChangeMyView* arguments by humans and three LLMs, our method reveals clear patterns: LLM-generated counter-arguments show lower type-token and lemma-token ratios but higher emotional intensity {---} particularly in anticipation and trust. They more closely resemble textbook-quality arguments {---} cogent, justified, explicitly respectful toward others, and positive in tone. Moreover, counter-arguments generated by LLMs converge more closely with the original post{'}s style and quality than those written by humans. Finally, we demonstrate that these differences enable a lightweight, interpretable, and highly effective classifier for detecting LLM-generated comments in CMV."
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<abstract>Distinguishing LLM-generated text from human-written is a key challenge for safe and ethical NLP, particularly in high-stake settings such as persuasive online discourse. While recent work focuses on detection, real-world use cases also demand interpretable tools to help humans understand and distinguish LLM-generated texts. To this end, we present an analysis framework comparing human- and LLM-authored arguments using two easily-interpretable feature sets: general-purpose linguistic features (e.g., lexical richness, syntactic complexity) and domain-specific features related to argument quality (e.g., logical soundness, engagement strategies). Applied to */r/ChangeMyView* arguments by humans and three LLMs, our method reveals clear patterns: LLM-generated counter-arguments show lower type-token and lemma-token ratios but higher emotional intensity — particularly in anticipation and trust. They more closely resemble textbook-quality arguments — cogent, justified, explicitly respectful toward others, and positive in tone. Moreover, counter-arguments generated by LLMs converge more closely with the original post’s style and quality than those written by humans. Finally, we demonstrate that these differences enable a lightweight, interpretable, and highly effective classifier for detecting LLM-generated comments in CMV.</abstract>
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%0 Conference Proceedings
%T AI Argues Differently: Distinct Argumentative and Linguistic Patterns of LLMs in Persuasive Contexts
%A Dönmez, Esra
%A Maurer, Maximilian
%A Lapesa, Gabriella
%A Falenska, Agnieszka
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F donmez-etal-2025-ai
%X Distinguishing LLM-generated text from human-written is a key challenge for safe and ethical NLP, particularly in high-stake settings such as persuasive online discourse. While recent work focuses on detection, real-world use cases also demand interpretable tools to help humans understand and distinguish LLM-generated texts. To this end, we present an analysis framework comparing human- and LLM-authored arguments using two easily-interpretable feature sets: general-purpose linguistic features (e.g., lexical richness, syntactic complexity) and domain-specific features related to argument quality (e.g., logical soundness, engagement strategies). Applied to */r/ChangeMyView* arguments by humans and three LLMs, our method reveals clear patterns: LLM-generated counter-arguments show lower type-token and lemma-token ratios but higher emotional intensity — particularly in anticipation and trust. They more closely resemble textbook-quality arguments — cogent, justified, explicitly respectful toward others, and positive in tone. Moreover, counter-arguments generated by LLMs converge more closely with the original post’s style and quality than those written by humans. Finally, we demonstrate that these differences enable a lightweight, interpretable, and highly effective classifier for detecting LLM-generated comments in CMV.
%U https://aclanthology.org/2025.emnlp-main.1755/
%P 34583-34614
Markdown (Informal)
[AI Argues Differently: Distinct Argumentative and Linguistic Patterns of LLMs in Persuasive Contexts](https://aclanthology.org/2025.emnlp-main.1755/) (Dönmez et al., EMNLP 2025)
ACL