RAG models have been successfully applied to various natural language processing tasks, including:
- Text summarization: RAG models can generate more accurate and informative summaries by combining relevant passages from the original text. This is particularly useful for tasks that require summarizing long documents or articles.
- Question answering: RAG models can retrieve relevant passages and generate answers based on the context. This is useful for tasks that require answering specific questions or providing detailed information.
- Language translation: RAG models can retrieve relevant passages in the target language and generate translations based on the context. This is useful for tasks that require translating text from one language to another.