@inproceedings{lan-etal-2025-mu,
    title = "目标自适应的可解释立场检测:新任务及大模型实验",
    author = "Lan, Yi  and
      王子豪, 王子豪  and
      Chen, Bo  and
      Zhao, Xiaobing",
    editor = "Sun, Maosong  and
      Duan, Peiyong  and
      Liu, Zhiyuan  and
      Xu, Ruifeng  and
      Sun, Weiwei",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-1.23/",
    pages = "298--310",
    abstract = "``传统立场检测通常假设目标已知,且仅输出立场类别(支持,反对,中立),难以应对目标不确定、立场判断需要有具体依据的情形。为此,本文提出目标自适应的可解释立场检测新任务,定义模型的输出为目标、观点和立场标签。具体地,构建了首个中文高质量立场检测数据集,并设计多维评估标准;评估了多种大语言模型的基线性能。实验发现:DeepSeek-V3在目标识别与立场分类表现最优,GPT-4o在观点生成上领先;大语言模型在目标明确时具备较强目标自适应能力,但处理存在反讽现象的输入时性能下降。数据集和实验结果公布于https://github.com/Cassieyy1102/TAISD。''"
}