English(EN) Adapting Reinforcement Learning with Chain-of-Thought Supervision for Explainable Detection of Hateful and Propagandistic Memes

新AI方法改进了仇恨表情包的检测和解释

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-16 04:00

研究人员开发了一种新方法，使用强化学习和链式思考（CoT）监督来改进仇恨和宣传表情包的检测和解释。该方法通过优化分类准确性和生成解释的质量来增强多模态大型语言模型（MLLMs）。在英语和阿拉伯语基准上的实验显示，准确性显著提高，并通过自然语言理由提供了更均衡的每类性能。 AI

影响这项研究提供了一种新颖的方法来增强AI识别和解释表情包中有害内容的能力，有可能改进内容审核系统。

排序理由该集群包含一篇详细介绍AI模型训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍AI模型训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Mohamed Bayan Kmainasi, Mucahid Kutlu, Ali Ezzat Shahroor, Abul Hasnat, Firoj Alam · 2026-06-16 04:00

使用链式思维监督的强化学习适应方法，用于可解释地检测仇恨和宣传性表情包

arXiv:2606.15307v1 Announce Type: cross Abstract: Hateful and propagandistic memes exploit the interplay between images and text to convey harmful intent that neither modality reveals alone. Although thinking-based multimodal large language models (MLLMs) have advanced vision-lan…