We address the task of predicting the gain of using RAG (retrieval augmented generation) for question answering with respect to not using it. We study the performance of a few pre-retrieval and post-retrieval predictors originally devised for ad hoc retrieval. We also study a few post-generation predictors, one of which is novel to this study and posts the best prediction quality. Our results show that the most effective prediction approach is a novel supervised predictor that explicitly models the semantic relationships among the question, retrieved passages, and the generated answer.

翻译：我们研究预测在使用RAG（检索增强生成）进行问答时相较于不使用RAG的增益。本文探讨了若干原本用于即席检索的检索前和检索后预测器的性能。同时，我们还研究了若干生成后预测器，其中包含本研究首次提出的新型预测器，该预测器在预测质量上表现最佳。实验结果表明，最有效的预测方法是一种新型监督式预测器，它能对不同元素（问题、检索段落和生成答案）之间的语义关系进行显式建模。