Multiple Language

Multiple language processing in natural language processing (NLP) focuses on developing computational models that understand and generate text across diverse languages, aiming to overcome limitations of single-language systems. Current research emphasizes improving the robustness and fairness of large language models (LLMs) in multilingual settings, often employing techniques like retrieval-augmented generation (RAG), reinforcement learning from human feedback (RLHF), and contrastive learning, while also addressing biases and the impact of multilingual training on model performance. This field is crucial for bridging the digital divide, enabling broader access to information and technology, and advancing cross-cultural understanding through improved machine translation, information retrieval, and other NLP applications.