Retrieval-augmented generation (RAG) frame work is showing state-of-the-art
performance on open-domain question answering tasks by referencing external
knowledge. However, the RAG system faces challenges with performance
degradation when it is fed contexts of low relevance or when the relative
relevance among the input contexts is inaccurately assessed. In this work, we
propose a RE-RAG framework that injects an explicit context relevance estimator
(RE) into the RAG system. RE-RAG re-evaluates the retrieved contexts with the
proposed context RE and passes the more relevant contexts along with their
measure importance to the generator. To train context RE, we propose an
unsupervised learning method, which does not utilize any labeled document
ranking data to train the context RE. To examine the efficacy of RE-RAG, we
examine its performance on Natural Questions and TriviaQA datasets. RE-RAG
achieves on-par performance compared to the FiD variants while utilizing fewer
contexts (0.25x). We show that the proposed context RE, which was trained with
the T5 model, is also applicable to RAG with LLMs(ChatGPT) by improving the
performance on NQ (+6.4EM) and TQA (+2.8EM), respecitvely. Lastly, we display
that RE can add interpretability to RAG framework as RE score highly correlates
with the RE-RAG accuracy. Consequently, RE can be utilized to filter out
unanswerable scenarios where context does not contain answers with 38.9%-51.3%
accuracy just by examining a set of retrieved contexts.

提出了一种 RE-RAG 框架，通过将显式的上下文相关性估计器（RE）注入到 RAG 系统中，重新评估检索到的上下文，并将更相关的上下文与其重要性量度传递给生成器，从而在不使用标记的文档排名数据训练上下文相关性估计器的情况下，实现与 FiD 变体相当的性能，并改进了 NQ 和 TQA 的性能以及提高了 RE-RAG 的准确性。