# Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach
## 1 Introduction  
With the rapid proliferation of social media and online platforms, stance detection has emerged as a core task within the field of natural language processing (NLP). Stance detection aims to automatically identify the attitude or stance toward a specific target expressed in text--such as social events, political figures, or policies--typically categorized into three classes: support, opposition, or neutrality. As the speed and complexity of information dissemination continue to escalate, efficiently analyzing and understanding public attitude trends within vast datasets has become a critical research topic across multiple disciplines, including social sciences, political science, and business analytics. Particularly in social media contexts, text is often characterized by its brevity, informality, emotional richness, and inherent biases, which significantly heighten the technical challenges of stance detection [1, 2, 3, 4]. Consequently, developing efficient methods capable of accurately capturing textual stances holds not only academic value but also profound practical significance for applications such as public opinion monitoring and market analysis. Traditional stance detection methods predominantly rely on supervised learning, utilizing large volumes of annotated data to train models for identifying stances toward specific targets. However, the process of collecting high-quality annotated data is both time-consuming and costly, a challenge that becomes even more pronounced when addressing domain-specific or newly emerging targets [4]. To overcome this bottleneck, zero-shot stance detection (ZSSD) [5; 6] has been developed. The primary objective of ZSSD is to determine the stance of text aimed at unknown targets without requiring annotated data specific to the target task [5]. The significance of this approach lies in its ability to break free from the dependency on extensive annotated datasets inherent in traditional supervised learning, substantially reducing data preparation costs and enhancing model generalization across emerging topics or cross-domain tasks. For instance, during sudden events (e.g., pandemics or political crises), ZSSD can swiftly adapt to new targets, enabling researchers or decision-makers to promptly capture public attitudes without awaiting the completion of data annotation processes [7]. Consequently, ZSSD offers a novel research paradigm for stance detection in theoretical terms and demonstrates broad practical potential in applications such as real-time public opinion analysis and cross-lingual stance identification. Although ZSSD holds significant potential in addressing data scarcity, its practical application is still hindered by numerous challenges that limit its accuracy and robustness. Firstly, the absence of annotated data specific to the target task makes it difficult for models to learn semantic features directly related to particular targets, resulting in unstable performance on unknown targets [8; 9]. For instance, traditional deep learning models rely on target-specific training samples; when confronted with unseen events or entities, they often fail to accurately comprehend the deeper connections between the text and the target. Secondly, the diversity and complexity of social media text further exacerbates the difficulties of ZSSD. This text frequently encompasses informal expressions, metaphors, sarcasm, and even multilingual mixtures, increasing the likelihood of models misjudging the author's true stance [10; 11]. For example, a sarcastic comment may superficially appear to support a viewpoint while its actual stance is one of opposition--a subtle distinction that existing methods often struggle to discern. Additionally, many current ZSSD approaches depend on transfer learning or prompt engineering to achieve zero-shot capabilities, yet these techniques remain inadequate in capturing the deep semantic relationships between text and target [12].