Modified Chain-of-Stance Prompting for LLMs: Enhancing Stance Detection under Low-Resource Conditions

Resumen

Despite the importance of performance of large language models (LLMs) in multilingual and low-resource settings, most work on stance detection has focused on supervised text classification in English. This paper revisits the task through in-context learning (ICL) method, introducing a novel method called modified Chain-of-Stance (mCoS) and assessing whether open-source LLMs can serve as competitive, zero-training alternatives to supervised systems. This work evaluates four instruction-tuned LLMs on Spanish and Catalan tweets from the Catalonia Independence Corpus (CIC). It also evaluates them on Basque (Euskera) data from VaxxStance and English tweets from SemEval-2016. The study compares five prompting strategies: zero-shot, few-shot, chain-of-thought, modified Chain-of-Stance, and the COLA multi-agent framework, and the results are compared against strong supervised baselines. The paper concludes that mCoS achieves state-of-the-art performance in all languages evaluated for generative models, but remains below fine-tuned and statistical baselines in the multilingual settings of Spanish, Catalan and Basque. Additional analysis shows that annotation noise and language-linked biases for the CIC dataset drive many errors, highlighting persistent challenges in multilingual stance detection and the need for higher-quality and fairness-oriented evaluation resources.