MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model

Large Language Models (LLMs) often struggle with hallucinations and outdated information.To address this, Information Retrieval (IR) systems can be employed to augment LLMs with up-to-date knowledge.However, existing IR techniques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.