Induce, Align, Predict: Zero-Shot Stance Detection via Cognitive Inductive Reasoning
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| Format: | Preprint |
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2025
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| _version_ | 1866917222310477824 |
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| author | Zhang, Bowen Ma, Jun Niu, Fuqiang Dong, Li Cao, Jinzhou Dai, Genan |
| author_facet | Zhang, Bowen Ma, Jun Niu, Fuqiang Dong, Li Cao, Jinzhou Dai, Genan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_13470 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Induce, Align, Predict: Zero-Shot Stance Detection via Cognitive Inductive Reasoning Zhang, Bowen Ma, Jun Niu, Fuqiang Dong, Li Cao, Jinzhou Dai, Genan Computation and Language I.2.7, I.2.6 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. |
| title | Induce, Align, Predict: Zero-Shot Stance Detection via Cognitive Inductive Reasoning |
| topic | Computation and Language I.2.7, I.2.6 |
| url | https://arxiv.org/abs/2506.13470 |