Retrieval-Augmented Generation for Question Answering: Architecture, Evaluation, and Future Directions
Abstract
Large Language Models (LLMs) are powerful for both natural language understanding and natural language generation. Yet, they are limited by their training data, which quickly becomes outdated or incorrect. Retrieval Augmented Generation (RAG) is a paradigm that combines a neural language model with a retrieval module that dynamicly retrieves relevant documents from an external corpus during generation. In this paper, we explore the architecture of such systems, break down their core componenets, and outline how to evaluate them. This includes an in-depth look at current open domain question answering approaches in the field, how the retrieval and generation components interact with each other, and a general methodology for building and evaluating such systems. We showcase state-of-the-art results on several open domain question answering datasets, where we demonstrate that RAG generates factually correct and reliable answers, whereas vanilla LLMs often hallucinate incorrect answers.
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