Zero-Shot Stance Detection in the Wild: Dynamic Target Generation and Multi-Target Adaptation
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912865944862720 |
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| author | Li, Aohua Zhang, Yuanshuo Gao, Ge Chen, Bo Zhao, Xiaobing |
| author_facet | Li, Aohua Zhang, Yuanshuo Gao, Ge Chen, Bo Zhao, Xiaobing |
| contents | Current stance detection research typically relies on predicting stance based on given targets and text. However, in real-world social media scenarios, targets are neither predefined nor static but rather complex and dynamic. To address this challenge, we propose a novel task: zero-shot stance detection in the wild with Dynamic Target Generation and Multi-Target Adaptation (DGTA), which aims to automatically identify multiple target-stance pairs from text without prior target knowledge. We construct a Chinese social media stance detection dataset and design multi-dimensional evaluation metrics. We explore both integrated and two-stage fine-tuning strategies for large language models (LLMs) and evaluate various baseline models. Experimental results demonstrate that fine-tuned LLMs achieve superior performance on this task: the two-stage fine-tuned Qwen2.5-7B attains the highest comprehensive target recognition score of 66.99%, while the integrated fine-tuned DeepSeek-R1-Distill-Qwen-7B achieves a stance detection F1 score of 79.26%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19802 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Zero-Shot Stance Detection in the Wild: Dynamic Target Generation and Multi-Target Adaptation Li, Aohua Zhang, Yuanshuo Gao, Ge Chen, Bo Zhao, Xiaobing Computation and Language Current stance detection research typically relies on predicting stance based on given targets and text. However, in real-world social media scenarios, targets are neither predefined nor static but rather complex and dynamic. To address this challenge, we propose a novel task: zero-shot stance detection in the wild with Dynamic Target Generation and Multi-Target Adaptation (DGTA), which aims to automatically identify multiple target-stance pairs from text without prior target knowledge. We construct a Chinese social media stance detection dataset and design multi-dimensional evaluation metrics. We explore both integrated and two-stage fine-tuning strategies for large language models (LLMs) and evaluate various baseline models. Experimental results demonstrate that fine-tuned LLMs achieve superior performance on this task: the two-stage fine-tuned Qwen2.5-7B attains the highest comprehensive target recognition score of 66.99%, while the integrated fine-tuned DeepSeek-R1-Distill-Qwen-7B achieves a stance detection F1 score of 79.26%. |
| title | Zero-Shot Stance Detection in the Wild: Dynamic Target Generation and Multi-Target Adaptation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.19802 |