@inproceedings{zhang-etal-2026-srcb,
    title = "{SRCB} at {S}em{E}val-2026 Task 5 A Multi-Target Finetuning Framework for Large Language Models with Joint Regression and Text Generation",
    author = "Zhang, Yuming  and
      Zhou, Junyu  and
      Li, Hongyu  and
      Zhang, Yongwei  and
      Jiang, Shanshan  and
      Dong, Bin",
    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-1.246/",
    pages = "1957--1964",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents our winning system for SemEval-2026 Task 5 on rating the plausibility of word senses in ambiguous stories. Unlike traditional Word Sense Disambiguation, the task requires predicting continuous plausibility scores that reflect human variability rather than selecting a single correct sense. We propose a multi-target fine-tuning framework for decoder-only large language models that jointly optimizes regression for score prediction and text generation for interpretable explanations. To address inter-annotator variability, we adopt objective-level strategies to enhance robustness. Our system achieves first place, demonstrating the effectiveness of unified regressive{--}generative modeling for fine-grained plausibility estimation."
}