@inproceedings{upravitelev-etal-2026-hermeneutichools,
    title = "hermeneutichools at {S}em{E}val-2026 Task 4: Multiperspectivity as a Resource for Narrative Similarity Prediction",
    author = "Upravitelev, Max  and
      Solopova, Veronika  and
      Yang, Jing  and
      Jakob, Charlott  and
      Sahitaj, Premtim  and
      Sahitaj, Ariana  and
      Schmitt, Vera",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.230/",
    pages = "1817--1831",
    ISBN = "979-8-89176-414-9",
    abstract = "Predicting narrative similarity can be under-stood as an inherently interpretive task: differ-ent, equally valid readings of the same text canproduce divergent interpretations and thus dif-ferent similarity judgments, posing a fundamen-tal challenge for semantic evaluation bench-marks that encode a single ground truth. Ratherthan treating this multiperspectivity as a chal-lenge to overcome, we propose to incorporateit in the decision making process of predic-tive systems. To explore this strategy, we cre-ated an ensemble of 31 LLM personas. Theserange from practitioners following interpretiveframeworks to more intuitive, lay-style charac-ters. Our experiments were conducted on theSemEval-2026 Task 4 dataset, where the sys-tem ranked 13th out of 47 teams and achievedan accuracy score of 0.705. Accuracy improveswith ensemble size, consistent with CondorcetJury Theorem-like dynamics under weakenedindependence. Practitioner personas performworse individually but produce less correlatederrors, yielding larger ensemble gains undermajority voting. Our error analysis reveals aconsistent negative association between gender-focused interpretive vocabulary and accuracyacross all persona categories, suggesting ei-ther attention to dimensions not relevant for thebenchmark or valid interpretations absent fromthe ground truth. This finding underscores theneed for evaluation frameworks that accountfor interpretive plurality."
}