MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model
<|reference_start|>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System through Multi-Result Ranking Model: Large Language Models (LLMs) often struggle with hallucinations and outdated information.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.<|reference_end|>
@article{khamnuansin2024mrrank:,
    title={MrRank: Improving Question Answering Retrieval System through
  Multi-Result Ranking Model},
    author={Danupat Khamnuansin, Tawunrat Chalothorn, Ekapol Chuangsuwanich},
    journal={arXiv preprint arXiv:2406.05733},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05733},
    primaryClass={cs.CL}
}