In this survey, we systematically analyze techniques used to adapt large multimodal models (LMMs) for low-resource (LR) languages, examining approaches ranging from visual enhancement and data creation to cross-modal transfer and fusion strategies. Through a comprehensive analysis of 117 studies across 96 LR languages, we identify key patterns in how researchers tackle the challenges of limited data and computational resources. We categorize works into resource-oriented and method-oriented contributions, further dividing contributions into relevant sub-categories. We compare method-oriented contributions in terms of performance and efficiency, discussing benefits and limitations of representative studies. We find that visual information often serves as a crucial bridge for improving model performance in LR settings, though significant challenges remain in areas such as hallucination mitigation and computational efficiency. In summary, we provide researchers with a clear understanding of current approaches and remaining challenges in making LMMs more accessible to speakers of LR (understudied) languages. We complement our survey with an open-source repository available at: https://github.com/marianlupascu/LMM4LRL-Survey.

翻译：本综述系统分析了将大型多模态模型（LMMs）适配于低资源（LR）语言所采用的技术，考察了从视觉增强、数据创建到跨模态迁移与融合策略等多种方法。通过对涵盖96种低资源语言的117项研究进行全面分析，我们识别了研究人员应对有限数据和计算资源挑战的关键模式。我们将现有工作归类为资源导向型与方法导向型贡献，并进一步将贡献细分为相关子类别。我们比较了方法导向型贡献在性能与效率方面的表现，讨论了代表性研究的优势与局限。我们发现，在低资源场景下，视觉信息常作为提升模型性能的关键桥梁，但在幻觉缓解与计算效率等领域仍存在显著挑战。总而言之，本研究为研究人员提供了关于当前方法以及使LMMs更易于低资源（研究不足）语言使用者所面临剩余挑战的清晰认识。我们通过开源仓库（https://github.com/marianlupascu/LMM4LRL-Survey）对本综述进行了补充。