Investigating on the External Knowledge in RAG for Zero-Shot Cross-Language Transfer

In the field of multilingual natural language processing (NLP), zero-shot cross-language transfer is an important research direction, which aims to enable models to effectively learn and reason without target language training data. This study explores the role of external knowledge in the Retrieval-Augmented Generation (RAG) model to improve the performance of zero-shot cross-lingual transfer. This paper proposes a new model architecture that enriches the knowledge base of the RAG model by integrating external knowledge bases, thereby enhancing its information bridging capabilities between source and target languages. In the experimental section, this paper conducts experiments using multiple cross-language tasks, including machine translation, question answering, and text summarization, to evaluate the performancen and domain of the model in different languages. The experimental results indicate that introducing external knowledge sources significantly improves the accuracy and robustness of the model, especially in resource-scarce language pairs. This research not only provides an effective solution for zero-shot cross-language transfer, but also provides new insights into understanding the role of external knowledge in improving the performance of NLP models.