Stance detection predicts attitudes towards targets in texts and has gained attention with the rise of social media. Traditional approaches include conventional machine learning, early deep neural networks, and pre-trained fine-tuning models. However, with the evolution of very large pre-trained language models (VLPLMs) like ChatGPT (GPT-3.5), traditional methods face deployment challenges. The parameter-free Chain-of-Thought (CoT) approach, not requiring backpropagation training, has emerged as a promising alternative. This paper examines CoT's effectiveness in stance detection tasks, demonstrating its superior accuracy and discussing associated challenges.

翻译：立场检测旨在预测文本中对特定目标的立场，并随着社交媒体的兴起而受到广泛关注。传统方法包括传统机器学习、早期深度神经网络以及预训练微调模型。然而，随着ChatGPT（GPT-3.5）等超大规模预训练语言模型（VLPLMs）的发展，传统方法面临部署挑战。无需反向传播训练的无参数思维链（CoT）方法已成为一种有前景的替代方案。本文研究了CoT在立场检测任务中的有效性，展示了其优越的准确性，并讨论了相关挑战。