@inproceedings{khamnuansin-etal-2024-mrrank,
title = "{M}r{R}ank: Improving Question Answering Retrieval System through Multi-Result Ranking Model",
author = "Khamnuansin, Danupat and
Chalothorn, Tawunrat and
Chuangsuwanich, Ekapol",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.282/",
doi = "10.18653/v1/2024.findings-acl.282",
pages = "4750--4762",
abstract = "Large Language Models (LLMs) often struggle with hallucinations and outdated information.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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<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Large Language Models (LLMs) often struggle with hallucinations and outdated information.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.</abstract>
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%0 Conference Proceedings
%T MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model
%A Khamnuansin, Danupat
%A Chalothorn, Tawunrat
%A Chuangsuwanich, Ekapol
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F khamnuansin-etal-2024-mrrank
%X 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.
%R 10.18653/v1/2024.findings-acl.282
%U https://aclanthology.org/2024.findings-acl.282/
%U https://doi.org/10.18653/v1/2024.findings-acl.282
%P 4750-4762
Markdown (Informal)
[MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model](https://aclanthology.org/2024.findings-acl.282/) (Khamnuansin et al., Findings 2024)
ACL