Language Alignment

Language alignment focuses on bridging the semantic gap between different languages and modalities, aiming to improve the performance and cross-lingual capabilities of large language models (LLMs). Current research emphasizes techniques like cross-lingual instruction tuning, Nash learning with adaptive feedback, and hierarchical graph tokenization to achieve better alignment, often leveraging parallel data and incorporating human feedback or preference models. These advancements are crucial for building more robust and inclusive LLMs, enabling improved multilingual applications in areas such as machine translation, bug localization, and cross-cultural communication.