Reasoner Outperforms: Generative Stance Detection with Rationalization for Social Media
Jiaqing Yuan[^yuan] (Amazon, New York, USA — jordanyuan111@gmail.com), Ruijie Xi[^xi] (Meta, Bellevue, WA, USA — rxi@ncsu.edu), and Munindar P Singh (Computer Science, North Carolina State University, Raleigh, NC, USA — mpsingh@ncsu.edu)
[^yuan]: Jiaqing Yuan's work was performed while he was at NC State University.
[^xi]: Ruijie Xi's work was performed while she was at NC State University.
Abstract
Stance detection is vital for promoting a trustworthy, human-centric Web by identifying biased or harmful narratives in user-generated content. Whereas recent LLM-based methods excel in accuracy, they often lack interpretability. We propose a generative stance detection approach that outputs explicit rationales and distills them into smaller language models (SLMs) via single-task and multi-task learning. Our method enables Flan-T5 to outperform GPT-3.5 zero-shot by up to 9.57%. We further show that rationales enhance multitask performance and improve distillation fidelity, advancing the development of transparent, fair, and trustworthy NLP systems.
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