Stance detection on social media is challenging for Large Language Models
(LLMs), as emerging slang and colloquial language in online conversations often
contain deeply implicit stance labels. Chain-of-Thought (COT) prompting has
recently been shown to improve performance on stance detection tasks --
alleviating some of these issues. However, COT prompting still struggles with
implicit stance identification. This challenge arises because many samples are
initially challenging to comprehend before a model becomes familiar with the
slang and evolving knowledge related to different topics, all of which need to
be acquired through the training data. In this study, we address this problem
by introducing COT Embeddings which improve COT performance on stance detection
tasks by embedding COT reasonings and integrating them into a traditional
RoBERTa-based stance detection pipeline. Our analysis demonstrates that 1) text
encoders can leverage COT reasonings with minor errors or hallucinations that
would otherwise distort the COT output label. 2) Text encoders can overlook
misleading COT reasoning when a sample's prediction heavily depends on
domain-specific patterns. Our model achieves SOTA performance on multiple
stance detection datasets collected from social media.

社交媒体上的立场检测对大型语言模型（LLMs）来说是具有挑战性的，因为在线对话中的新兴俚语和口头语通常包含深层次的隐含立场标签。在这项研究中，我们通过引入 COT 嵌入来改进了 COT 提示在立场检测任务上的性能，将 COT 推理嵌入到传统的基于 RoBERTa 的立场检测流程中，从而解决了隐含立场识别的问题。我们的分析表明：1）文本编码器可以利用 COT 推理，即使存在轻微错误或幻觉，否则会扭曲 COT 的输出标签。2）当样本的预测严重依赖于领域特定的模式时，文本编码器可能会忽视误导性的 COT 推理。我们的模型在多个社交媒体收集的立场检测数据集上实现了 SOTA 性能。