@inproceedings{adjei-aryal-2026-howard,
    title = "{H}oward {U}niversity-{AI}4{PC} at {S}em{E}val-2026 Task 7: Culturally Aware Multilingual Model Routing Through a Mixture-of-Specialists Framework",
    author = "Adjei, Isaac  and
      Aryal, Saurav",
    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-1.327/",
    pages = "2597--2602",
    ISBN = "979-8-89176-414-9",
    abstract = "SemEval-2026 Task 7 (BLEnD) evaluates culturally contextual multiple-choice reasoning across 26 languages and 30 geographic regions, emphasizing everyday knowledge, cultural norms, and region-specific variations in language use. This paper presents the Howard University{--}AI4PC system, a Phase{\textasciitilde}1 implementation of a culturally aware Mixture-of-Specialists (MoS) framework designed to improve multilingual cultural reasoning without requiring large-scale fine-tuning. Our approach integrates four key components: (1) linguistic and regional metadata extraction for identifying language, dialect, and cultural context; (2) a hierarchical routing strategy that selects the most culturally aligned model path; (3) Model Control Prompting (MCP), which injects region-aware constraints, dialectal hints, and output-format controls; and (4) a lightweight retrieval-augmented layer that supplies culturally specific factual cues. Although specialist LoRA/QLoRA adapters are planned for future phases, the routing and prompting layers alone achieve 80.01{\textbackslash}{\%} accuracy on 47{\{},{\}}014 test MCQs, demonstrating that cultural grounding and linguistically informed routing substantially enhance performance even in the absence of trained experts. We summarize the task, describe the system in detail, present quantitative and qualitative analyses, and outline next-stage extensions involving specialist model training and expanded cultural knowledge integration."
}