发表机构
Jaypee University of Information Technology; Mohamed bin Zayed University of Artificial Intelligence; Thapar Institute of Engineering & Technology; Dr. B. R. Ambedkar National Institute of Technology Jalandhar（贾皮尔信息技术大学; 穆罕默德·本·扎耶德人工智能大学; 塔帕尔工程技术学院; 贾朗达尔阿姆贝德卡尔博士国家理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对多模态歧义下性别化经济梗推理的认知过度承诺偏差，提出CGER-Net框架，在EconMeme-GE数据集上使歧义实例的性别过度承诺率降低最多44%，且79%的生成理由符合证据要求。
AI 中文摘要
多模态梗理解越来越多地用于分析社会敏感内容，但现有模型在歧义情境下解释经济依赖和社会角色时往往表现出偏差行为。许多梗通过稀疏文本或符号视觉线索表达经济关系，为性别归因提供的证据不足。在这种欠明确的情境中，模型倾向于依赖预训练关联，导致产生幻觉和刻板印象的经济角色分配。本研究从语境充分性角度探究图文梗中的性别化经济依赖，识别出认知过度承诺——在证据不足时推断角色——是偏差的主要来源。我们提出CGER-Net，一种基于语境的多模态框架，该框架会评估输入是否为性别化经济推理提供了充分证据，并应用证据门控推理，在线索明确时支持可靠归因，否则倾向于原则性弃权（不执行）。我们在EconMeme-GE数据集上评估CGER-Net，该数据集是经过整理的图文梗数据集，标注为男性、女性、中性或歧义。在强大的当代多模态基线中，CGER-Net在歧义实例上将性别过度承诺率降低了多达44%，同时在明确案例上保持了相当的准确率。人工评估进一步显示，79%的生成理由被判断为与可用证据在认知上一致。这些结果凸显了在可靠且负责任的多模态分析中，对何时不应进行推断进行建模的重要性。
英文摘要
Multimodal meme understanding is increasingly used to analyze socially sensitive content, yet existing models often exhibit biased behavior when interpreting economic dependence and social roles under ambiguity. Many memes express economic relationships through sparse text or symbolic visual cues, providing insufficient evidence for gendered attribution. In such underspecified settings, models tend to rely on pretraining correlations, leading to hallucinated and stereotypical economic role assignments. In this work, we study gendered economic dependence in image-text memes through the lens of contextual sufficiency and identify epistemic overcommitment-inferring roles without adequate evidence-as a primary source of bias. We propose CGER-Net, a context-grounded multimodal framework that estimates whether the input provides sufficient evidence for gendered economic reasoning and applies evidence-gated inference to enable confident attribution when cues are explicit while favoring principled abstention otherwise. We evaluate CGER-Net on EconMeme-GE, a curated dataset of image-text memes annotated as Men, Women, Neutral, or Ambiguous. Across strong contemporary multimodal baselines, CGER-Net reduces Gender Overcommitment Rate by up to 44% on ambiguous instances while maintaining comparable accuracy on unambiguous cases. Human evaluation further shows that 79% of generated rationales are judged as epistemically aligned with the available evidence. These results highlight the importance of modeling when not to infer for reliable and responsible multimodal analysis.
