  Large Language Models (LLMs) often struggle with hallucinations and outdated
information. To address this, Information Retrieval (IR) systems can be
employed to augment LLMs with up-to-date knowledge. However, existing IR
techniques contain deficiencies, posing a performance bottleneck. Given the
extensive array of IR systems, combining diverse approaches presents a viable
strategy. Nevertheless, prior attempts have yielded restricted efficacy. In
this work, we propose an approach that leverages learning-to-rank techniques to
combine heterogeneous IR systems. We demonstrate the method on two Retrieval
Question Answering (ReQA) tasks. Our empirical findings exhibit a significant
performance enhancement, outperforming previous approaches and achieving
state-of-the-art results on ReQA SQuAD.
