E fficient RAG : Efficient Retriever for Multi-Hop Question Answering (2024)

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).

## introduction large-language models (llms) have shown remarkable performance in numerous applications and tasks @xcite @xcite . however, llms lack knowledge underrepresented in their training data, especially in domain-specific settings, and still face the issues of hallucinations @xcite @xcite . retrievalaugmented generation (rag) techniques @xcite have been widely adapted to retrieve knowledge from external resources to ground the generated responses. previous rag methods often adapt one-round retrieval, e.g., only use the user query or question as the input to retrieve knowledge @xcite @xcite . such one-round rag is capable of answering questions which clearly state all the needed information in the input query @xcite @xcite , such as onehop question, e.g., "what is newton's third law ## conclusion in this study, we introduce the efficientrag retriever, a novel approach for multi-hop question retrieval that iteratively generates new queries while circumventing the need for large language models. evaluations across three benchmark datasets demonstrate that efficientrag not only achieves high recall with a minimal number of retrieved chunks but also delivers promising outcomes in subsequent question-answering tasks. these findings indicate that efficientrag outperforms traditional retrieval-augmented generation methods, particularly in the context of complex, multi-hop question-answering scenarios. fangkai yang, pu zhao, zezhong wang, lu wang, bo qiao, jue zhang, mohit garg, qingwei lin, saravan rajmohan, and dongmei zhang. 2023. empower large language model to perform better on industrial domain-specific question answering. in proceedings of the 2023 conference on empirical methods in natural language processing: emnlp 2023 -industry track, singapore, december 6-10, 2023, pages 294-312. association for computational linguistics. zhilin yang, peng qi, saizheng zhang, yoshua bengio, william w. cohen, ruslan salakhutdinov, and christopher d. manning. 2018a. hotpotqa: a dataset for diverse, explainable multi-hop question answering. in proceedings of the 2018 conference on empirical methods in natural language processing, brussels, belgium, october 31 -november 4, 2018, pages 2369-2380. association for computational linguistics. zhilin yang, peng qi, saizheng zhang, yoshua bengio, william w. cohen, ruslan salakhutdinov, and christopher d. manning. 2018b. hotpotqa: a dataset for diverse, explainable multi-hop question answering. in conference on empirical methods in natural language processing (emnlp). yue zhang, yafu li, leyang cui, deng cai, lemao liu, tingchen fu, xinting huang, enbo zhao, yu zhang, yulong chen, longyue wang, anh tuan luu, wei bi, freda shi, and shuming shi. 2023. siren's song in the ai ocean: a survey on hallucination in large language models. corr, abs/2309.01219. tong zhu, junfei ren, zijian yu, mengsong wu, guoliang zhang, xiaoye qu, wenliang chen, zhefeng wang, baoxing huai, and min zhang. 2023. mirror: a universal framework for various information extraction tasks. arxiv:2311.05419 [cs].