@inproceedings{mattie-etal-2026-x,
    title = "{X}-{NLP} at {S}em{E}val-2026 Task 12: Prompting {LLM}s for Abductive Event Reasoning",
    author = "Mattie, Caelen  and
      Bowen, Patrick  and
      King, Milton",
    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-1.289/",
    pages = "2281--2289",
    ISBN = "979-8-89176-414-9",
    abstract = "In this work, we applied two different systems to the SemEval 2026 Shared Task 12, which exploresabductive event reasoning. Specifically, this task involves determining the cause of an event from a list of candidate causes. Instances are accompanied with documents that can provide applicable knowledge for the target event. Both of our systems involve prompting LLMS and our best performing system leverages retrieval-augmented generation. Our best performing system achieved a score of 84{\%} and ranked 40th out of the 221 total submissions."
}