# Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach
## 6 Conclusions  
In this paper, we propose a novel stance detection method based on multi-agent debate, termed ZSMD (zero-shot stance detection based on multi-agent debate). This \begin{table}
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**Tweet and Background Knowledge** & **Target** & **Stance** & **Predict** & **Reason for the Error** \\ \hline \hline Tweet: Where is the childcare program & & & & \\ @[PERSON] which you said would be in place? & & & & \\ Background knowledge: The tweet mentions [PERSON], an Irish Labour Party politician who promised childcare support for single-parent families. “#7 istoyoung” is a campaign hashtag opposing the removal of single mothers with children over 7 years old from the welfare system. The tweet focuses on social support policies for single parents but does not directly address the topic of abortion legalization. & Legalization of Abortion & Against & None & Implicit stance expressions \\ \hline \hline Tweet: @[PERSON]@JudgeBambi. There’s no capacity for discourse when you assume ppl are your enemy. Hate. Misery. Paranoia. A waste. Background knowledge: The feminist movement focuses on gender equality, emphasizing women’s rights across various domains. Some critics argue that the positions of certain feminists are overly extreme, overlooking the possibility of building constructive dialogue with others, and instead fostering hostility and unnecessary confrontation. & Feminist Movement & None & Against & Insufficient logical reasoning capability \\ \hline \hline \end{tabular}
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Table 4: Representative error cases in zero-shot stance detection on SemEval-2016 Task 6. This table presents two typical examples of stance misclassification, each corresponding to a distinct source of error: (1) implicit stance expressions, which obscure the true intent of the text, and (2) insufficient logical reasoning by the model during argument generation. Figure 4: Error analysis of DeepSeek-v3 on SemEval-2016. approach introduces two debater agents representing opposing stances (supporting and opposing), which generate initial arguments under the enhancement of background knowledge. Through multiple rounds of debate, these agents iteratively refine their viewpoints, culminating in a final stance determination by a referee agent based on the debate process. The experimental results demonstrate that ZSMD achieves superior performance on the classic stance detection datasets SemEval-2016 and P-Stance, while also exhibiting strong generalization capabilities. In future work, we aim to further enhance the reasoning and debating abilities of the agents to address more complex stance expressions. Additionally, we plan to explore more effective knowledge augmentation strategies, such as integrating large language models (LLMs) for adaptive knowledge retrieval, to improve the relevance and accuracy of background knowledge. Furthermore, we are interested in incorporating multimodal information (e.g., images or videos) to improve the model's understanding of rich social media content. However, we acknowledge that the lack of publicly available, well-annotated multimodal stance detection datasets, as well as the increased model complexity and training cost, present significant challenges. We consider addressing these limitations as important directions for future research. Conceptualization, C.W. ; data curation, J.M. and C.W. ; formal analysis, L.R. and Y.X. ; funding acquisition, Y.X. ; investigation, C.W. and B.W. ; methodology, J.M., L.R. and Y.X. ; project administration, B.W. ; resources, J.M. and B.W. ; software, J.M. and C.W. ; validation, J.M. ; writing--original draft, J.M. and C.W. ; writing--review and editing, L.R. and Y.X. All authors have read and agreed to the published version of the manuscript. The research was supported by the National Natural Science Foundation of China under Grant No. 62102372, the Project of Science and Technology in Henan Province under Grant Nos.