Zero-shot stance detection (ZSSD) aims to detect stances toward unseen
targets. Incorporating background knowledge to enhance transferability between
seen and unseen targets constitutes the primary approach of ZSSD. However,
these methods often struggle with a knowledge-task disconnect and lack logical
consistency in their predictions. To address these issues, we introduce a novel
approach named Logically Consistent Chain-of-Thought (LC-CoT) for ZSSD, which
improves stance detection by ensuring relevant and logically sound knowledge
extraction. LC-CoT employs a three-step process. Initially, it assesses whether
supplementary external knowledge is necessary. Subsequently, it uses API calls
to retrieve this knowledge, which can be processed by a separate LLM. Finally,
a manual exemplar guides the LLM to infer stance categories, using an if-then
logical structure to maintain relevance and logical coherence. This structured
approach to eliciting background knowledge enhances the model's capability,
outperforming traditional supervised methods without relying on labeled data.

Zero-shot stance detection enhanced by logically consistent chain-of-thought and supplementary external knowledge outperforms traditional supervised methods without relying on labeled data.