@inproceedings{nguyen-etal-2025-persuasive,
    title = "How Persuasive Is Your Context?",
    author = "Nguyen, Tu  and
      Du, Kevin  and
      Hoyle, Alexander Miserlis  and
      Cotterell, Ryan",
    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://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1633/",
    pages = "32085--32111",
    ISBN = "979-8-89176-332-6",
    abstract = "Two central capabilities of language models (LMs) are: (i) drawing on prior knowledge about entities, which allows them to answer queries such as What{'}s the official language of Austria?, and (ii) adapting to new information provided in context, e.g., Pretend the official language of Austria is Tagalog., that is pre-pended to the question. In this article, we introduce targeted persuasion score (TPS), designed to quantify how persuasive a given context is to an LM where persuasion is operationalized as the ability of the context to alter the LM{'}s answer to the question. In contrast to evaluating persuasiveness only through a model{'}s most likely answer, TPS provides a more fine-grained view of model behavior. Based on the Wasserstein distance, TPS measures how much a context shifts a model{'}s original answer distribution towarda target distribution. Empirically, through aseries of experiments, we show that TPS captures a more nuanced notion of persuasiveness than previously proposed metrics."
}