被ICML 2026收录，提出针对低资源语言的LLM鲁棒多语言适配方法，增强跨语言泛化能力。
arXiv:2510.14466v3 Announce Type: replace Abstract: Large language models (LLMs) continue to struggle with low-resource languages, primarily due to li…
用非洲低资源语言玩多轮对话，成功绕过ChatGPT、Gemini等主流大模型的安全护栏，安全漏洞新发现。
arXiv:2605.18239v1 Announce Type: new Abstract: Large Language Models (LLMs) remain vulnerable to jailbreak attempts that circumvent safety guardrails…
探讨多模态大模型在低资源语言环境下的实际构建挑战与方案
arXiv:2605.17152v1 Announce Type: new Abstract: Multimodal LLMs are evolving from vision-language to tri-modality that see, hear, and read, yet pipeli…
揭秘低资源语言大模型中的专家路由：对比Transformer与Mamba混合架构的MoE表现。
arXiv:2605.17598v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enable efficient model scaling, yet expert routing behavior acr…
首个系统性评测大模型在南亚12种印度语言上安全性的基准，揭示低资源语言安全漏洞。
arXiv:2603.17915v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are deployed in multilingual settings, their safety behavior…