让小模型生成解释性理由，显著提升立场识别效果。
Reasoner Outperforms: Generative Stance Detection with Rationalization for Social Media
- 用生成式方法让模型输出可理解的推理过程。
- 小模型性能超越GPT-3.5零样本，最高提升9.57%。
- 真实推理有助于知识蒸馏，适合可信AI应用。
立场检测对构建以人为本的网络环境至关重要，能识别用户生成内容中的偏见与有害叙事。随着大语言模型的发展，现有方法多将立场检测视为分类任务，虽提升了复杂群体互动建模能力，但缺乏可解释性，难以提供透明的预测依据。本文采用生成式方法，使立场预测包含显式的可解释推理，并通过单任务和多任务学习将推理融入小型语言模型（FlanT5）。结果表明，引入推理后，小模型性能超越GPT-3.5零样本表现，最高提升达9.57%。同时，推理能力增强多任务学习效果，但在单任务设置中可能降低性能。更重要的是，真实推理能有效促进知识蒸馏至小模型，推动可解释、可信系统的发展，助力应对歧视、建立信任、促进社交媒体上的公平参与。
原文摘要 · Abstract (English)
Stance detection is crucial for fostering a human-centric Web by analyzing user-generated content to identify biases and harmful narratives that undermine trust. With the development of Large Language Models (LLMs), existing approaches treat stance detection as a classification problem, providing robust methodologies for modeling complex group interactions and advancing capabilities in natural language tasks. However, these methods often lack interpretability, limiting their ability to offer transparent and understandable justifications for predictions. This study adopts a generative approach, where stance predictions include explicit, interpretable rationales, and integrates them into smaller language models through single-task and multitask learning. We find that incorporating reasoning into stance detection enables the smaller model (FlanT5) to outperform GPT-3.5's zero-shot performance, achieving an improvement of up to 9.57%. Moreover, our results show that reasoning capabilities enhance multitask learning performance but may reduce effectiveness in single-task settings. Crucially, we demonstrate that faithful rationales improve rationale distillation into SLMs, advancing efforts to build interpretable, trustworthy systems for addressing discrimination, fostering trust, and promoting equitable engagement on social media.
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