Chinese Task

Research on Chinese language processing focuses on improving the performance of large language models (LLMs) on various tasks, including machine translation, reading comprehension, and instruction following. Current efforts concentrate on addressing challenges specific to Chinese, such as character representation, limited training data, and cultural nuances, employing techniques like attention mechanisms, stroke sequence modeling, and data augmentation strategies within transformer-based architectures. These advancements aim to enhance the generalizability and accuracy of LLMs for Chinese, impacting fields like cross-lingual communication, natural language understanding, and the development of more inclusive AI systems.