@inproceedings{lucy-etal-2025-tell,
    title = "Tell, Don{'}t Show: Leveraging Language Models' Abstractive Retellings to Model Literary Themes",
    author = "Lucy, Li  and
      Griffiths, Camilla  and
      Levine, Sarah  and
      Eberhardt, Jennifer L  and
      Demszky, Dorottya  and
      Bamman, David",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.1162/",
    pages = "22585--22610",
    ISBN = "979-8-89176-256-5",
    abstract = "Conventional bag-of-words approaches for topic modeling, like latent Dirichlet allocation (LDA), struggle with literary text. Literature challenges lexical methods because narrative language focuses on immersive sensory details instead of abstractive description or exposition: writers are advised to *show, don{'}t tell*. We propose Retell, a simple, accessible topic modeling approach for literature. Here, we prompt resource-efficient, generative language models (LMs) to *tell* what passages *show*, thereby translating narratives' surface forms into higher-level concepts and themes. By running LDA on LMs' retellings of passages, we can obtain more precise and informative topics than by running LDA alone or by directly asking LMs to list topics. To investigate the potential of our method for cultural analytics, we compare our method{'}s outputs to expert-guided annotations in a case study on racial/cultural identity in high school English language arts books."
}