@inproceedings{zhou-etal-2026-uir,
    title = "uir-cis at {S}em{E}val-2026 Task 12: Mitigating Prior-Induced Hallucinations in Retrieval-Augmented Reasoning via Precision-Oriented Decoding",
    author = "Zhou, Chiyao  and
      Wang, Zebing  and
      Deng, Kexin  and
      Zhao, Yaru  and
      Deng, Lin  and
      Li, Binyang",
    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-1.415/",
    pages = "3337--3342",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our system for the SemEval-2026 Task 12 on Abductive Event Reasoning (AER). We systematically address the ``over-selection'' hallucination pathology in Instruction-tuned Large Language Models (LLMs), where models erroneously align distractors with semantic priors rather than retrieved evidence. Our framework utilizes a 32-billion parameter Qwen2.5 foundational model adapted via Low-Rank Adaptation (LoRA) and evaluated under a Zero-shot Chain-of-Thought (CoT) setting. To mitigate epistemic noise, we propose a Precision-Oriented Decoding (POD) strategy that couples low-temperature sampling (T=0.45) with scaled majority voting (K=9). Following a three-stage empirical evolution{---}from baseline diagnosis to precision optimization and ensemble analysis{---}our system achieved a score of 0.802 on the official test set. Our findings demonstrate that in causal reasoning tasks with strict penalization for incorrect predictions, epistemic noise suppression is strictly superior to heuristic recall compensation."
}