Zero-shot stance detection (ZSSD) seeks to determine the stance of text toward previously unseen targets, a task critical for analyzing dynamic and polarized online discourse with limited labeled data. While large language models (LLMs) offer zero-shot capabilities, prompting-based approaches often fall short in handling complex reasoning and lack robust generalization to novel targets. Meanwhile, LLM-enhanced methods still require substantial labeled data and struggle to move beyond instance-level patterns, limiting their interpretability and adaptability. Inspired by cognitive science, we propose the Cognitive Inductive Reasoning Framework (CIRF), a schema-driven method that bridges linguistic inputs and abstract reasoning via automatic induction and application of cognitive reasoning schemas. CIRF abstracts first-order logic patterns from raw text into multi-relational schema graphs in an unsupervised manner, and leverages a schema-enhanced graph kernel model to align input structures with schema templates for robust, interpretable zero-shot inference. Extensive experiments on SemEval-2016, VAST, and COVID-19-Stance benchmarks demonstrate that CIRF not only establishes new state-of-the-art results, but also achieves comparable performance with just 30% of the labeled data, demonstrating its strong generalization and efficiency in low-resource settings.

翻译：零样本立场检测（ZSSD）旨在判定文本对先前未见目标的立场，这一任务对于在标注数据有限的情况下分析动态且两极分化的在线话语至关重要。尽管大语言模型（LLMs）具备零样本能力，但基于提示的方法通常在处理复杂推理方面表现不足，且对新颖目标缺乏稳健的泛化能力。同时，LLM增强方法仍需大量标注数据，且难以超越实例级模式，限制了其可解释性与适应性。受认知科学启发，我们提出了认知归纳推理框架（CIRF），这是一种模式驱动的方法，通过自动诱导和应用认知推理模式，桥接语言输入与抽象推理。CIRF以无监督方式从原始文本中抽象出一阶逻辑模式，构建多关系模式图，并利用模式增强的图核模型将输入结构与模式模板对齐，以实现稳健、可解释的零样本推理。在SemEval-2016、VAST和COVID-19-Stance基准上的大量实验表明，CIRF不仅取得了最新的最优结果，而且仅使用30%的标注数据即可达到相当的性能，证明了其在低资源场景下的强大泛化能力与高效性。