Multilingual Text

Multilingual text processing focuses on developing computational methods to understand and utilize text across multiple languages, aiming to overcome limitations of English-centric models. Current research emphasizes improving large language models (LLMs) for multilingual tasks, including translation, text generation, and knowledge extraction, often employing techniques like instruction tuning, contrastive learning, and mixture-of-experts architectures. This field is crucial for bridging linguistic divides in information access, fostering cross-cultural understanding, and enabling the development of more inclusive and equitable AI applications. Furthermore, research addresses challenges like bias mitigation and the effective handling of low-resource languages.