We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We design a multi-faceted prompt to extract a textual explanation of the reasoning that connects visible cues to underlying social meanings. These extracted explanations or rationales serve as augmentations to the conversational text to facilitate dialogue understanding and transfer. Our empirical results over 2,340 experimental settings demonstrate the significant positive impact of adding these rationales. Our findings hold true for in-domain classification, zero-shot, and few-shot domain transfer for two different social meaning detection tasks, each spanning two different corpora.

翻译：我们提出了一种可泛化的分类方法，该方法利用大型语言模型（LLMs）来促进检测对话中隐含编码的社会意义。我们设计了一个多方面的提示，以提取连接可见线索与潜在社会意义的推理过程的文本解释。这些提取的解释或推理依据作为对话文本的增强信息，以促进对话理解和迁移。我们在2,340个实验设置上的实证结果表明，添加这些推理依据具有显著的积极影响。我们的发现在两个不同的社会意义检测任务中均成立，每个任务涵盖两个不同的语料库，包括领域内分类、零样本和少样本领域迁移。