@inproceedings{zhuang-etal-2024-efficientrag,
title = "{E}fficient{RAG}: Efficient Retriever for Multi-Hop Question Answering",
author = "Zhuang, Ziyuan and
Zhang, Zhiyang and
Cheng, Sitao and
Yang, Fangkai and
Liu, Jia and
Huang, Shujian and
Lin, Qingwei and
Rajmohan, Saravan and
Zhang, Dongmei and
Zhang, Qi",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.199",
pages = "3392--3411",
abstract = "Retrieval-augmented generation (RAG) methods encounter difficulties when addressing complex questions like multi-hop queries.While iterative retrieval methods improve performance by gathering additional information, current approaches often rely on multiple calls of large language models (LLMs).In this paper, we introduce EfficientRAG, an efficient retriever for multi-hop question answering.EfficientRAG iteratively generates new queries without the need for LLM calls at each iteration and filters out irrelevant information.Experimental results demonstrate that EfficientRAG surpasses existing RAG methods on three open-domain multi-hop question-answering datasets.The code is available in [aka.ms/efficientrag](https://github.com/NIL-zhuang/EfficientRAG-official).",
}
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<abstract>Retrieval-augmented generation (RAG) methods encounter difficulties when addressing complex questions like multi-hop queries.While iterative retrieval methods improve performance by gathering additional information, current approaches often rely on multiple calls of large language models (LLMs).In this paper, we introduce EfficientRAG, an efficient retriever for multi-hop question answering.EfficientRAG iteratively generates new queries without the need for LLM calls at each iteration and filters out irrelevant information.Experimental results demonstrate that EfficientRAG surpasses existing RAG methods on three open-domain multi-hop question-answering datasets.The code is available in [aka.ms/efficientrag](https://github.com/NIL-zhuang/EfficientRAG-official).</abstract>
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%0 Conference Proceedings
%T EfficientRAG: Efficient Retriever for Multi-Hop Question Answering
%A Zhuang, Ziyuan
%A Zhang, Zhiyang
%A Cheng, Sitao
%A Yang, Fangkai
%A Liu, Jia
%A Huang, Shujian
%A Lin, Qingwei
%A Rajmohan, Saravan
%A Zhang, Dongmei
%A Zhang, Qi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhuang-etal-2024-efficientrag
%X Retrieval-augmented generation (RAG) methods encounter difficulties when addressing complex questions like multi-hop queries.While iterative retrieval methods improve performance by gathering additional information, current approaches often rely on multiple calls of large language models (LLMs).In this paper, we introduce EfficientRAG, an efficient retriever for multi-hop question answering.EfficientRAG iteratively generates new queries without the need for LLM calls at each iteration and filters out irrelevant information.Experimental results demonstrate that EfficientRAG surpasses existing RAG methods on three open-domain multi-hop question-answering datasets.The code is available in [aka.ms/efficientrag](https://github.com/NIL-zhuang/EfficientRAG-official).
%U https://aclanthology.org/2024.emnlp-main.199
%P 3392-3411
Markdown (Informal)
[EfficientRAG: Efficient Retriever for Multi-Hop Question Answering](https://aclanthology.org/2024.emnlp-main.199) (Zhuang et al., EMNLP 2024)
ACL
- Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, and Qi Zhang. 2024. EfficientRAG: Efficient Retriever for Multi-Hop Question Answering. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 3392–3411, Miami, Florida, USA. Association for Computational Linguistics.