Abstract
Stance detection is an active task in natural language processing (NLP) that aims to identify the author’s stance towards a particular target within a text. Given the remarkable language understanding capabilities and encyclopedic prior knowledge of large language models (LLMs), how to explore the potential of LLMs in stance detection has received significant attention. Unlike existing LLM-based approaches that focus solely on fine-tuning with large-scale datasets, we propose a new prompting method, called Chain of Stance (CoS). In particular, it positions LLMs as expert stance detectors by decomposing the stance detection process into a series of intermediate, stance-related assertions that culminate in the final judgment. This approach leads to significant improvements in classification performance. We conducted extensive experiments using four SOTA LLMs on the SemEval 2016 dataset, covering the zero-shot and few-shot learning setups. The results indicate that the proposed method achieves state-of-the-art results with an F1 score of 79.84 in the few-shot setting.
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Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: Semeval-2016 task 6: detecting stance in tweets. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 31–41 (2016)
Achiam, J., et al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)
Bai, J., et al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)
Touvron, H., et al.: Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)
Wang, X., Wang, Y., Cheng, S., Li, P., Liu, Y.: Deem: dynamic experienced expert modeling for stance detection. arXiv preprint arXiv:2402.15264 (2024)
Li, A., et al.: Mitigating biases of large language models in stance detection with calibration. arXiv preprint arXiv:2402.14296 (2024)
Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. In: Advances in Neural Information Processing Systems, vol. 35, pp. 24824–24837 (2022)
Hardalov, M., Arora, A., Nakov, P., Augenstein, I.: A survey on stance detection for MIS-and disinformation identification. arXiv preprint arXiv:2103.00242 (2021)
Li, Y., Caragea, C.: Target-aware data augmentation for stance detection (2021)
Mets, M., Karjus, A., Ibrus, I., Schich, M.: Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media. PLoS ONE 19(4), e0302380 (2024)
İlker Gül, Lebret, R., Aberer, K.: Stance detection on social media with fine-tuned large language models (2024)
Yao, Z., Yang, W., Wei, F.: Enhancing zero-shot stance detection with contrastive and prompt learning. Entropy 26(4), 325 (2024)
Hardalov, M., Arora, A., Nakov, P., Augenstein, I.: Few-shot cross-lingual stance detection with sentiment-based pre-training. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36 (2022)
Ding, D., Fu, X., Peng, X., Fan, X., Huang, H., Zhang, B.: Leveraging chain-of-thought to enhance stance detection with prompt-tuning. Mathematics 12(4), 568 (2024)
Zhu, Y., et al.: Short text classification with soft knowledgeable prompt-tuning. Expert Syst. Appl. 246, 123248 (2024)
Huang, H., et al.: Knowledge-enhanced prompt-tuning for stance detection. ACM Trans. Asian Low-Resourc. Lang. Inf. Process. 22(6), 1–20 (2023)
Liang, B., et al.: JointCL: a joint contrastive learning framework for zero-shot stance detection. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 81–91. Association for Computational Linguistics (2022)
Hanley, H.W., Durumeric, Z.: Tata: stance detection via topic-agnostic and topic-aware embeddings. arXiv preprint arXiv:2310.14450 (2023)
Li, A., Liang, B., Zhao, J., Zhang, B., Yang, M., Xu, R.: Stance detection on social media with background knowledge. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 15703–15717 (2023)
Cheng, Y., Zhang, Q., Shi, C., Xiao, L., Hao, S., Hu, L.: COSD: collaborative stance detection with contrastive heterogeneous topic graph learning. arXiv preprint arXiv:2404.17609 (2024)
Lan, X., Gao, C., Jin, D., Li, Y.: Stance detection with collaborative role-infused LLM-based agents. arXiv preprint arXiv:2310.10467 (2023)
Acknowledgments
This work is supported by Fellowship from the China Postdoctoral Science Foundation (2023M733907), Natural Science Foundation of Hunan Province of China (242300421412), Foundation of Key Laboratory of Dependable Service Computing in Cyber-Physical-Society (Ministry of Education), Chongqing University (PJ.No: CPSDSC202103), Industrial Science and Technology Research Project of Henan Province (222102210024).
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Ma, J., Wang, C., Xing, H., Zhao, D., Zhang, Y. (2025). Chain of Stance: Stance Detection with Large Language Models. In: Wong, D.F., Wei, Z., Yang, M. (eds) Natural Language Processing and Chinese Computing. NLPCC 2024. Lecture Notes in Computer Science(), vol 15363. Springer, Singapore. https://doi.org/10.1007/978-981-97-9443-0_7
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