@inproceedings{pauli-etal-2025-measuring,
    title = "Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language",
    author = "Pauli, Amalie Brogaard  and
      Augenstein, Isabelle  and
      Assent, Ira",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.naacl-long.506/",
    pages = "10056--10075",
    ISBN = "979-8-89176-189-6",
    abstract = "We are exposed to much information trying to influence us, such as teaser messages, debates, politically framed news, and propaganda {---} all of which use persuasive language. With the recent interest in Large Language Models (LLMs), we study the ability of LLMs to produce persuasive text. As opposed to prior work which focuses on particular domains or types of persuasion, we conduct a general study across various domains to measure and benchmark to what degree LLMs produce persuasive language - both when explicitly instructed to rewrite text to be more or less persuasive and when only instructed to paraphrase. We construct the new dataset Persuasive-Pairs of pairs of a short text and its rewrite by an LLM to amplify or diminish persuasive language. We multi-annotate the pairs on a relative scale for persuasive language: a valuable resource in itself, and for training a regression model to score and benchmark persuasive language, including for new LLMs across domains. In our analysis, we find that different {\textquoteleft}personas' in LLaMA3{'}s system prompt change persuasive language substantially, even when only instructed to paraphrase."
}