@inproceedings{schmerle-hellwig-2026-schmerle,
    title = "schmerle at {S}em{E}val-2026 Task 4: Exploring Large Language Model Prompting Strategies for Low-Resource Narrative Similarity Detection",
    author = "Schmerle, Maximilian  and
      Hellwig, Nils Constantin",
    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-1.257/",
    pages = "2046--2056",
    ISBN = "979-8-89176-414-9",
    abstract = "Narrative similarity detection has broad applications in plagiarism detection, content recommendation, and comparative narrative analysis. We present a training-free, prompting-only framework for SemEval-2026 Task 4 (Track A), which requires identifying which of two candidate stories is narratively more similar to a given anchor story. Without any fine-tuning or additional annotations, we systematically evaluate three prompt templates across five structural prompting strategies, including zero-shot and few-shot inference, narrative summarization, keyword extraction, aspect splitting, and pairwise comparison. Structured prompt templates and decomposed pairwise comparisons consistently outperform baseline configurations, achieving a peak accuracy of 72.50{\%} on the test set and 67.75{\%} on the final leaderboard (23th out of 44 teams)."
}