RAR: A Refined-Attitude Reasoning Framework on LLMs for Zero Shot Stance Detection

Yiming Lee, Yangfu Zhu, Xinming Chen, Bin Wu

Abstract: In the context of the rapid evolution of social media, zero-shot stance detection has become an essential task in Natural Language Processing, particularly in effectively identifying attitudes toward unseen targets in texts using large language models. However, existing methods have not fully taken advantage of Large Language Models (LLMs) capabilities in complex reasoning and contextual understanding, often leading to cumbersome approaches. This paper introduces a new framework named Refined-Attitude Reasoning (RAR), which combines example reasoning and attitude refinement techniques to significantly improve the performance of LLMs on stance detection tasks. Specifically, RAR first constructs a case library using information from the training dataset, retrieving examples similar to new input contexts by calculating a mixed weighted representation of text-target pairs. Second, it refines the expression of attitudes through backward reasoning from the cases and extracts broader attitude words to enhance the accuracy of stance identification in texts. Finally, during the generation phase, it combines optimized attitude words with examples for forward stance reasoning and implements a checking mechanism to ensure the accuracy and rationality of the generated results. Experiments show that our RAR framework achieves an overall F1 score exceeding that of other baseline methods on the zero-shot stance detection dataset VAST, demonstrating the importance of fine-grained attitude refinement and in-context learning in enhancing model performance.

External IDs:dblp:conf/ijcnn/LeeZCW25