@inproceedings{dao-sy-etal-2026-hcmus-repeatedgames,
    title = "{HCMUS} {R}epeated{G}ames at {S}em{E}val-2026 Task 12: {C}ausal{RAG}: Synergizing Causal Graph Retrieval and Extended {L}o{RA} for Abductive Reasoning",
    author = "Dao Sy, Duy Minh  and
      Tran, Nguyen  and
      Huynh, Trung Kiet  and
      Nguyen Lam, Phu Quy  and
      Pham, Phu Hoa",
    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-1.59/",
    pages = "409--417",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents our system developed for SemEval-2026 Task 12: Abductive Event Reasoning (AER). The shared task aims at identifying the most plausible cause of a real-world event from multiple-choice options, given retrieved documents as evidence. In this work, we propose using hybrid retrieval that combines BM25 keyword matching with dense semantic search to capture explicit causal keywords. Moreover, we apply extended LoRA fine-tuning that trains both attention and MLP layers of a 32-billion parameter language model with only 0.81{\%} trainable parameters. For final refinement, we perform development set fine-tuning to leverage validation data before inference. We achieve a tie for fifth place in the shared task: our system achieves a score of 0.90 on the official test set evaluation, ranking tied for fifth among participating teams and representing a +0.27 improvement over our baseline."
}