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

• URL: http://arxiv.org/abs/2406.05733v1
• Date: Sun, 9 Jun 2024 11:00:01 GMT
• Title: MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model
• Authors: Danupat Khamnuansin, Tawunrat Chalothorn, Ekapol Chuangsuwanich,
• Abstract summary: Large Language Models (LLMs) often struggle with hallucinations and outdated information.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. We propose an approach that leverages learning-to-rank techniques to combine heterogeneous IR systems.
• Score: 4.173772253427094
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) often struggle with hallucinations and outdated information.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.

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