@inproceedings{aryal-aryal-2026-ai4pc,
    title = "{AI}4{PC}-{H}oward {U}niversity at {S}em{E}val-2026 Task 9: Evaluating Teacher-Student Weak Supervision and Direct {LLM} Prompting for Multilingual Political Polarization Detection",
    author = "Aryal, Surangana  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-1.316/",
    pages = "2506--2511",
    ISBN = "979-8-89176-414-9",
    abstract = "We describe the AI4PC{--}Howard University submission to SemEval-2026 Task 9, Subtask 1 on multilingual political polarization detection across 22 languages. We investigated two approaches: (1) a weakly supervised teacher{--}student framework in which a large language model (LLM) generated pseudo-labels to train an XLM-RoBERTa-base classifier, and (2) (2) a context-engineered prompt-based approach using Meta-Llama-3.1-8B-Instruct. The teacher{--}student approach exhibited instability under distribution shift and collapsed toward majority predictions at test time. Consequently, our final submission used direct inference with Meta-Llama-3.1-8B-Instruct. While this approach produced competitive macro-F1 across evaluated languages, results reveal strong positive-class bias and substantial precision{--}recall imbalance. Our findings highlight limitations of weak supervision for subjective political tasks and underscore trade-offs between scalability, bias, and computational cost in LLM-only multilingual systems."
}