Here's my try:
The article discusses how recent advancements in natural language processing (NLP) have shifted from creating specific frameworks for specific tasks to using powerful general-purpose language models that can be fine-tuned for various applications. However, current efforts are still focused on tasks where a human could provide the solution without additional background knowledge. The author then goes on to describe their work on building an end-to-end differentiable model called Retrieval Augmented Generation (RAG), which combines an information retrieval component with a seq2seq generator. RAG can be fine-tuned on knowledge-intensive downstream tasks and achieves state-of-the-art results compared with even the largest pretrained seq2seq language models. Unlike these pretrained models, RAG's internal knowledge can be easily altered or supplemented on the fly, enabling researchers and engineers to control what RAG knows and doesn't know without wasting time or compute power retraining the entire model.
For example, when given the prompt "When did the first mammal appear on Earth?" RAG might retrieve documents for "Mammal," "History of Earth," and "Evolution" from a knowledge base, then use this information to generate a response such as "The first mammal appeared on Earth around 200 million years ago during the Mesozoic era." This approach allows RAG to leverage its internal knowledge to provide more accurate and informative responses compared with pretrained models that may only rely on surface-level features in the input text.