Zero Shot Stance Detection

Zero-shot stance detection aims to automatically classify the attitude expressed in text towards a topic without prior training on that specific topic. Current research focuses on leveraging large language models (LLMs), often incorporating techniques like data augmentation, adversarial learning, and chain-of-thought prompting, to improve the transferability of knowledge across different topics and languages. These advancements are significant for applications like fake news detection and opinion mining, where labeled data for every possible topic is scarce, and for cross-lingual understanding of online discourse. The field is actively exploring methods to mitigate biases inherent in LLMs and to enhance the robustness and generalizability of zero-shot stance detection models.