@inproceedings{shibata-etal-2026-curiosai,
    title = "{C}urios{AI} at {S}em{E}val-2026 Task 4: A Comprehensive Study of Zero-Shot versus Fine-Tuned Approaches for Narrative Similarity",
    author = "Shibata, Yuki  and
      Takushima, Hiroki  and
      Beppu, Fumika  and
      Manoj Kumar, Aiswariya  and
      Yamaga, Daichi  and
      Hori, Takayuki",
    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-1.62/",
    pages = "434--439",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents our system for SemEval-2026 Task 4 on narrative similarity assessment.Through comprehensive experimentation, we evaluated various approaches including zero-shot pre-trained models, prompt engineering with large language models, and multiple fine-tuning strategies using synthetic data. Our experiments revealed a surprising finding: pre-trained sentence transformers in a zero-shot setting consistently outperformed all fine-tuning attempts. Specifically, our best system using sentence-transformers/sentence-t5-xl achieved 67.5{\%} accuracy on the development set (95{\%} CI: [61.0{\%}, 74.0{\%}]), while all fine-tuning approaches resulted in performance degradation of 2-18 percentage points. We provide a detailed analysis of why fine-tuning failed and discuss the implications for narrative similarity tasks."
}