arXiv：cs.AI· Zeba Afroz, Harsh Vardhan, Md. Shad Akhtar·· 4 小时前
ReCoT：用标签引导 CoT 优化实现宣传检测推理
Reasoning for Propaganda Detection via Label-Guided CoT Optimisation
AI 导读
ReCoT 是一个通过群体级细化和性能引导重采样来捕捉说服技巧定义性线索的推理条件检测框架，无需额外解释标注。在 INDI-PROPspan 上其 macro F1_LCS 达 0.364，超过基线并达到 SFT 上限的 88%；在 PTC 上达到 SFT 上限 Avg F1_LCS 的 85%，同时保持可解释性。
正文
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 introduce ReCoT, a reasoning-conditioned detection framework that captures definitional cues of persuasive techniques via population-level refinement and performance-guided resampling, without requiring additional explanation annotations. On INDI-PROPspan, ReCoT achieves a macro F1_LCS of 0.364, surpassing baselines and reaching 88% of the SFT upper bound on Avg F1_LCS. Similarly, on PTC, ReCoT reaches 85% of the SFT upper bound on Avg F1_LCS, closing much of this gap while remaining interpretable. Importantly, the analysis of the reasoning pool's convergence and update pattern for ReCoT shows effective refinement of reasoning CoTs.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17271 [cs.AI] |
| (or arXiv:2610.17271v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17271 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zeba Afroz [view email]
[v1]
Mon, 12 Oct 2026 18:25:12 UTC (197 KB)
来源：arXiv：cs.AI · arxiv.org