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[Submitted on 9 Sep 2026] Title:Does Linguistic Structure Enrichment Enhance Coherence Assessment? Not With Current Architectures View a PDF of the paper titled Does Linguistic Structure Enrichment Enhance Coherence Assessment? Not With Current Architectures, by Victor Mazzotti and Luiz Pereira and Marina Bitencourt dos Santos and Helena Maia and Carlos Caetano and N\'adia Felix and Sandra Avila View PDF HTML (experimental) Abstract:Recent advances in large language models have transformed human-computer interaction. Despite their fluency, these models often produce texts that are grammatically correct but semantically incoherent, containing contradictions or disruptions in logical flow. This work investigates whether enriching text with syntactic and rhetorical information can improve incoherence prediction. Our experiments and analysis show that plain texts achieved higher accuracy because the added information was structurally and syntactically incompatible with the language model's architecture. Additionally, to demonstrate the practical importance of coherence assessment, we performed zero-shot experiments on a Brazilian disinformation dataset, suggesting that textual coherence can serve as a proxy for detecting misleading content. Code and models are available at this https URL. Comments: 6 figures, 8 tables, 10 pages Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.10893 [cs.CL] (or arXiv:2609.10893v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.10893 arXiv-issued DOI via DataCite (pending registration) Submission history From: Sandra Avila [view email] [v1] Wed, 9 Sep 2026 22:53:50 UTC (562 KB) Full-text links: Access Paper: View a PDF of the paper titled Does Linguistic Structure Enrichment Enhance Coherence Assessment? Not With Current Architectures, by Victor Mazzotti and Luiz Pereira and Marina Bitencourt dos Santos and Helena Maia and Carlos Caetano and N\'adia Felix and Sandra Avila View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)