GeAR: Graph-enhanced Agent for Retrieval-augmented Generation
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912443560624128 |
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| author | Shen, Zhili Diao, Chenxin Vougiouklis, Pavlos Merita, Pascual Piramanayagam, Shriram Chen, Enting Graux, Damien Melo, Andre Lai, Ruofei Jiang, Zeren Li, Zhongyang QI, YE Ren, Yang Tu, Dandan Pan, Jeff Z. |
| author_facet | Shen, Zhili Diao, Chenxin Vougiouklis, Pavlos Merita, Pascual Piramanayagam, Shriram Chen, Enting Graux, Damien Melo, Andre Lai, Ruofei Jiang, Zeren Li, Zhongyang QI, YE Ren, Yang Tu, Dandan Pan, Jeff Z. |
| contents | Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios. In this paper, we introduce GeAR, a system that advances RAG performance through two key innovations: (i) an efficient graph expansion mechanism that augments any conventional base retriever, such as BM25, and (ii) an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework. Our evaluation demonstrates GeAR's superior retrieval capabilities across three multi-hop question answering datasets. Notably, our system achieves state-of-the-art results with improvements exceeding 10% on the challenging MuSiQue dataset, while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems. The project page is available at https://gear-rag.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18431 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GeAR: Graph-enhanced Agent for Retrieval-augmented Generation Shen, Zhili Diao, Chenxin Vougiouklis, Pavlos Merita, Pascual Piramanayagam, Shriram Chen, Enting Graux, Damien Melo, Andre Lai, Ruofei Jiang, Zeren Li, Zhongyang QI, YE Ren, Yang Tu, Dandan Pan, Jeff Z. Computation and Language Artificial Intelligence Information Retrieval Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios. In this paper, we introduce GeAR, a system that advances RAG performance through two key innovations: (i) an efficient graph expansion mechanism that augments any conventional base retriever, such as BM25, and (ii) an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework. Our evaluation demonstrates GeAR's superior retrieval capabilities across three multi-hop question answering datasets. Notably, our system achieves state-of-the-art results with improvements exceeding 10% on the challenging MuSiQue dataset, while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems. The project page is available at https://gear-rag.github.io. |
| title | GeAR: Graph-enhanced Agent for Retrieval-augmented Generation |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2412.18431 |