RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation

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.