@inproceedings{ning-2026-locuprompt,
    title = "{L}ocu{P}rompt at {S}em{E}val-2026 Task 7: A Multilingual Prompting Framework for Cross-Cultural Everyday Knowledge in {LLM}s",
    author = "Ning, Ningjingke",
    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-1.134/",
    pages = "973--980",
    ISBN = "979-8-89176-414-9",
    abstract = "Understanding everyday cultural knowledge remains a fundamental challenge for large language models (LLMs). This paper presents LocuPrompt, a multilingual framework for SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures. To address Short Answer Questions (SAQ), we employ an English-pivot generation strategy with back-translation, combined with empirical locale-specific routing that dynamically assigns the optimal LLM to each target region. For Multiple-Choice Questions (MCQ), we apply parameter-efficient fine-tuning to a robust multilingual base model, utilizing locale-aware instructions that frame the LLM as a ``local resident.'' By integrating strategic model selection with resource-efficient adaptation, LocuPrompt effectively bridges cross-lingual cultural gaps while maintaining a fully reproducible pipeline."
}