发表机构
University of Minnesota, Twin Cities（明尼苏达大学双城分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出MODL方法，通过联合训练领域LoRA和语言LoRA并施加互正交约束，无需配对数据即可为英语遥感多模态大模型添加多语言能力，显著提升跨语言遥感问答准确率。
AI 中文摘要
遥感（RS）多模态大语言模型（MLLMs）仅以英语进行训练和评估，而纯文本指令数据覆盖了超过100种语言。我们提出了MODL（互正交领域-语言组合），这是一种无需任何多语言遥感示例即可为英语遥感MLLM添加新语言的方案：在英语遥感图像上训练的领域LoRA和在纯文本上训练的语言LoRA被联合学习，并在整个训练过程中通过一个损失项保持每一层的两个更新彼此正交。这一约束是该方案的关键成分。没有它，相同的训练能正确回答遥感问题但使用英语，会抹去基础模型的大部分多语言文本能力，并在三个种子中的一个上发散；十六种替代方案，从免训练合并到先前的正交变体，都以同样的方式失败。MODL修复了每个种子上的所有失败：答案在56-71%的情况下正确且使用目标语言，而最佳替代方案达到27%，大多数保持在8%以下；文本能力保持在未训练基础模型的水平，在西班牙语上超越了Qwen2.5-VL-7B，且零多语言-多模态数据。一个五语言适配器在三个种子上完整保留了英语、西班牙语和越南语的能力；非拉丁文字仍是一个开放的边界。
英文摘要
Remote-sensing (RS) multimodal large language models (MLLMs) are trained and evaluated only in English, while text-only instruction data covers over 100 languages. We propose MODL (Mutually Orthogonal Domain-Language composition), a recipe that adds new languages to an English RS MLLM without a single multilingual RS example: a domain LoRA trained on English RS imagery and a language LoRA trained on text alone are learned jointly, under one loss term that keeps the two updates mutually orthogonal at every layer throughout training. This constraint is the recipe's active ingredient. Without it, the same training answers RS questions correctly but in English, erases much of the base model's multilingual text ability, and diverges on one seed in three; sixteen alternatives, from training-free merging to prior orthogonality variants, fail the same way. MODL repairs every failure on every seed: answers are correct and in the target language 56-71% of the time, where the best alternative reaches 27% and most stay below 8%, text ability stays at the level of the untrained base, and on Spanish it surpasses Qwen2.5-VL-7B, with zero multilingual-multimodal data. A single five-language adapter retains English, Spanish, and Vietnamese at full strength across three seeds; non-Latin scripts remain an open boundary.
