@inproceedings{aryal-dhungana-2025-howard,
    title = "{H}oward {U}niversity-{AI}4{PC} at {S}em{E}val-2025 Task 10: Ensembling {LLM}s for Multi-lingual Multi-Label and Multi-Class Meta-Classification",
    author = "Aryal, Saurav  and
      Dhungana, Prasun",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.209/",
    pages = "1585--1592",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper describes our approach and submission to the SemEval 2025 shared task on ``Multilingual Characterization and Extraction of Narratives from Online News''. The purpose of this task was to assign primary and fine-grained roles to named entities in news articles from five different languages, on the topics of Climate Change and Ukraine-Russia War. In this paper, we explain how we approached the task by utilizing multiple LLMs via Prompt Engineering and combining their results into a final task result through an ensemble meta-classification technique. Our experimental results demonstrate that this integrated approach outperforms the provided baseline in detecting bias, deception, and manipulation in news media across multiple languages."
}