@inproceedings{jung-etal-2026-narrative,
    title = "Narrative Landscape: Mapping Narrative Dispositions Across {LLM}s",
    author = "Jung, Donghoon  and
      Choi, Jiwoo  and
      Chae, Songeun  and
      Jung, Seohyon",
    editor = {Hamilton, Sil  and
      {\"O}hman, Emily  and
      Hicke, Rebecca M. M.  and
      Bizzoni, Yuri  and
      Bax, Axel  and
      Matthews, Jacob A.  and
      H{\"a}m{\"a}l{\"a}inen, Mika},
    booktitle = "Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities",
    month = jul,
    year = "2026",
    address = "San Diego, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.nlp4dh-1.3/",
    pages = "24--30",
    ISBN = "979-8-89176-427-9",
    abstract = "This study proposes a quantitative framework for profiling LLM dispositions as stable, model-specific regularities in output under repeated, controlled elicitation. Using a structured narrative constraint-selection task administered across six frontier models and three instruction types, we operationalize disposition through two dimensions: ``consistency'', measured as cross-replication selection overlap via Jaccard similarity, and ``diversity'', measured as dispersion across options via the inverse Simpson index. We further introduce Narrative Landscape, a PCA-based visualization that maps each model{'}s selection profile into a shared space for direct comparison. Results reveal a clear rigidity{--}exploration spectrum across model families and show that instruction types shift the geometry of selection spaces even when scalar metrics appear similar, indicating that comparable scores can mask qualitatively distinct selection topologies."
}