@inproceedings{zhu-etal-2026-kdw,
    title = "{KDW} at {S}em{E}val-2026 Task 12: Logic-Driven Distillation with Knowledge Graphs for Efficient Abductive Reasoning",
    author = "Zhu, Sihan  and
      Wu, Hongjie  and
      Xu, Xinyan",
    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-1.124/",
    pages = "905--912",
    ISBN = "979-8-89176-414-9",
    abstract = "Large language models (LLMs) such as GPT-4 and Gemini show strong reasoning ability but incur substantial computational cost in abductive reasoning settings. We present our system for ``SemEval-2026 Task 12 {---} Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models'', which integrates knowledge graph (KG) evidence extraction with knowledge distillation to transfer structured reasoning from a large teacher model to a compact student model. Our approach ranks 8th in the shared task while achieving performance comparable to frontier LLMs at a fraction of the inference cost."
}