Towards Inclusive AI: Advancing Multilingual Large Language Models

Abstract

Large language models (LLMs) have advanced rapidly, yet their development remains disproportionately focused on a few high-resource languages, leaving fundamental scientific and societal questions about multilingual capability, safety, and equity unresolved. This talk examines multilingual LLMs as a lens for understanding these challenges. I will first discuss observations from large-scale evaluations with real-world natural data, which reveal substantial performance gaps and highlight the need to treat multilingualism as a multidimensional construct. I then turn to safety, presenting work that uncovers multilingual jailbreak vulnerabilities and introduces frameworks for achieving more consistent cross-lingual alignment. Building on analyses of language-specific internal mechanisms, I will outline new strategies for enhancing multilingual systems and describe open-source efforts such as the SeaLLMs and Babel projects that aim to broaden linguistic and cultural inclusivity. Finally, I will discuss emerging directions beyond language, including recent findings on abstract thought in LLMs, which point toward the development of models that are not only multilingual but genuinely multicultural and contextually grounded.