English(EN) Reasoning for Propaganda Detection via Label-Guided CoT Optimisation

新框架增强了宣传检测的可解释性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了ReCoT，一个旨在通过提高可解释性来改进宣传检测系统的新框架。该方法使用条件推理检测来识别说服性技巧的定义线索，而无需额外的解释性标注。ReCoT在INDI-PROPspan和PTC等基准数据集上表现强劲，在保持可解释性的同时，显著缩小了与监督微调上限的差距。 AI

影响增强了AI模型的可解释性，可能提高了在宣传检测等敏感应用中的信任度和透明度。

排序理由研究论文，详细介绍了AI模型可解释性的一种新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Zeba Afroz, Harsh Vardhan, Md. Shad Akhtar · 2026-10-15 04:00

通过标签引导的CoT优化进行宣传检测的推理

arXiv:2610.17271v1 Announce Type: new Abstract: Propaganda detection systems usually operate as black boxes, lacking explanations. To address this, we hypothesise that persuasive techniques contain definitional characteristics with respect to their social-political setting. We in…