EfficientRAG: Efficient Retriever for Multi-Hop Question Answering
Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
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).- Anthology ID:
- 2024.emnlp-main.199
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3392–3411
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.199/
- DOI:
- 10.18653/v1/2024.emnlp-main.199
- Cite (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.
- Cite (Informal):
- EfficientRAG: Efficient Retriever for Multi-Hop Question Answering (Zhuang et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.199.pdf