Retrieval Augmented Generation: Streamlining the creation of intelligent natural language processing models

Description

Summary

• ML APPLICATIONS OPEN SOURCE Teaching computers to understand how humans write and speak, known as natural language processing (NLP), is one of the oldest challenges in AI research.
• We found that RAG uses its nonparametric memory to “cue” the seq2seq model into generating correct responses, essentially combining the flexibility of the “closed-book” or parametric-only approach with the performance of “open-book” or retrieval-based methods.
• Combining a retrieval-based component with a generative component has advantages even in purely extractive tasks, such as the open-domain NaturalQuestions task.
• Hugging Face’s Transformers has become a de facto standard in open source NLP, thanks to its low barrier to entry and coverage of state-of-the-art models, and it integrates with the new Datasets library to provide the indexed knowledge source that RAG relies on.